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Alamaniotis, Miltiadis, and Georgios Karagiannis. "Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power." International Journal of Monitoring and Surveillance Technologies Research 5, no. 3 (July 2017): 1–14. http://dx.doi.org/10.4018/ijmstr.2017070101.
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This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.
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Abduljabbar, Rusul L., Hussein Dia, and Pei-Wei Tsai. "Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction." Journal of Advanced Transportation 2021 (March 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/5589075.
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This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.
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Montaser, Eslam, José-Luis Díez, and Jorge Bondia. "Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework." Sensors 21, no. 9 (May 4, 2021): 3188. http://dx.doi.org/10.3390/s21093188.
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Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient’s variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided—a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.
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Faria, Álvaro José Gomes de, Sérgio Henrique Godinho Silva, Leônidas Carrijo Azevedo Melo, Renata Andrade, Marcelo Mancini, Luiz Felipe Mesquita, Anita Fernanda dos Santos Teixeira, Luiz Roberto Guimarães Guilherme, and Nilton Curi. "Soils of the Brazilian Coastal Plains biome: prediction of chemical attributes via portable X-ray fluorescence (pXRF) spectrometry and robust prediction models." Soil Research 58, no. 7 (2020): 683. http://dx.doi.org/10.1071/sr20136.
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Portable X-ray fluorescence (pXRF) spectrometry has been successfully used for soil attribute prediction. However, recent studies have shown that accurate predictions may vary according to soil type and environmental conditions, motivating investigations in different biomes. Hence, this work attempted to accurately predict soil pH, sum of bases (SB), cation exchange capacity (CEC) at pH 7.0 and base saturation (BS) using pXRF-obtained data with high variability and robust prediction models in the Brazilian Coastal Plains biome. A total of 285 soil samples were collected to generate prediction models for A (n = 123), B (n = 162) and A+B (n = 285) horizons through stepwise multiple linear regression, support vector machine with linear kernel (SVM) and random forest. Data were divided into calibration (75%) and validation (25%) sets. Accuracy of the predictions was assessed by coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The A+B horizons dataset had optimal performance, especially for SB predictions using SVM, achieving R2 = 0.82, RMSE = 1.02 cmolc dm–3, MAE = 1.17 cmolc dm–3 and RPD = 2.33. The most important predictor variable was Ca. Predictions using pXRF data were accurate especially for SB. Limitations of the predictions caused by soil classes and environmental conditions should be further investigated in other regions.
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Goldstein, Benjamin A., Michael J. Pencina, Maria E. Montez-Rath, and Wolfgang C. Winkelmayer. "Predicting mortality over different time horizons: which data elements are needed?" Journal of the American Medical Informatics Association 24, no. 1 (June 29, 2016): 176–81. http://dx.doi.org/10.1093/jamia/ocw057.
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Objective: Electronic health records (EHRs) are a resource for “big data” analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment. Material and Methods: We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models. Results: The best predictors used all the available data (c-statistic ranged from 0.72–0.76), with stronger models in the near term. While different variable groups showed different utility, exclusion of any particular group did not lead to a meaningfully different risk assessment. Discrete time models performed better than time-to-event models. Conclusions: Different variable groups were predictive over different time horizons, with vital signs most predictive for near-term mortality and demographic and comorbidities more important in long-term mortality.
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Liu, Chengyuan, Josep Vehí, Parizad Avari, Monika Reddy, Nick Oliver, Pantelis Georgiou, and Pau Herrero. "Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal." Sensors 19, no. 19 (October 8, 2019): 4338. http://dx.doi.org/10.3390/s19194338.
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(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose–insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.
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Almarzooqi, Ameera M., Maher Maalouf, Tarek H. M. El-Fouly, Vasileios E. Katzourakis, Mohamed S. El Moursi, and Constantinos V. Chrysikopoulos. "A hybrid machine-learning model for solar irradiance forecasting." Clean Energy 8, no. 1 (January 10, 2024): 100–110. http://dx.doi.org/10.1093/ce/zkad075.
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Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
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Fernández Pozo, Rubén, Ana Belén Rodríguez González, Mark Richard Wilby, and Juan José Vinagre Díaz. "Analysis of Extended Information Provided by Bluetooth Traffic Monitoring Systems to Enhance Short-Term Level of Service Prediction." Sensors 22, no. 12 (June 17, 2022): 4565. http://dx.doi.org/10.3390/s22124565.
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Bluetooth monitoring systems (BTMS) have opened a new era in traffic sensing, providing a reliable, economical, and easy-to-deploy solution to uniquely identify vehicles. Raw data from BTMS have traditionally been used to calculate travel time and origin–destination matrices. However, we could extend this to include other information like the number of vehicles or their residence times. This information, together with their temporal components, can be applied to the complex task of forecasting traffic. Level of service (LOS) prediction has opened a novel research line that fulfills the need to anticipate future traffic states, based on a standard link-based variable, accepted for both researchers and practitioners. In this paper, we incorporate BTMS’s extended variables and temporal information to an LOS classifier based on a Random Undersampling Boost algorithm, which is proven to efficiently respond to the data unbalance intrinsic to this problem. By using this approach, we achieve an overall recall of 87.2% for up to 15-min prediction horizons, reaching 96.6% predicting congestion, and improving the results for the intermediate traffic states, especially complex given their intrinsic instability. Additionally, we provide detailed analyses on the impact of temporal information on the LOS predictor’s performance, observing improvements up to a separation of 50 min between last features and prediction horizons. Furthermore, we study the predictor importance resulting from the classifiers to highlight those features contributing the most to the final achievements.
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Wang, Haowei, Kin On Kwok, and Steven Riley. "Forecasting influenza incidence as an ordinal variable using machine learning." Wellcome Open Research 9 (January 8, 2024): 11. http://dx.doi.org/10.12688/wellcomeopenres.19599.1.
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Background: Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. Methods: We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 32 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). Results: The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Conclusion: Given that many natural extreme phenomena are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.
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Zjavka, Ladislav. "Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation." Energies 14, no. 22 (November 12, 2021): 7581. http://dx.doi.org/10.3390/en14227581.
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Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.
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Li, Gang, Lin Zhong, Wenjun Sun, Shaohua Zhang, Qianjie Liu, Qingsheng Huang, and Guoliang Hu. "A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs." Sensors 24, no. 21 (October 29, 2024): 6926. http://dx.doi.org/10.3390/s24216926.
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To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC.
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Giraldo, Sergio A. C., Príamo A. Melo, and Argimiro R. Secchi. "Tuning of Model Predictive Controllers Based on Hybrid Optimization." Processes 10, no. 2 (February 11, 2022): 351. http://dx.doi.org/10.3390/pr10020351.
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A tuning procedure for a model predictive controller (MPC) is presented for multi-input multi-output systems. The approach consists of two steps based on a hybrid method: the goal attainment method and a variable neighborhood search. In the first step, the weights of the MPC objective function are obtained, minimizing the square error between the closed-loop response of the internal controller model and a predefined desired reference trajectory. In the second step, the integer variables of the problem (prediction and control horizons) are obtained, minimizing the square error between the closed-loop response and an optimal trajectory, aiming a controller with low computational cost and good performance. The proposed method was tested in two benchmark processes using different MPC formulations, showing satisfactory results.
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Mendes, Wanderson de Sousa, and Michael Sommer. "Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration." Agronomy 13, no. 7 (July 6, 2023): 1800. http://dx.doi.org/10.3390/agronomy13071800.
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Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg−1, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management.
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Aslan, Antonio, José-Luis Díez, Alejandro José Laguna Sanz, and Jorge Bondia. "On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An In Silico Study." Applied Sciences 13, no. 9 (April 25, 2023): 5348. http://dx.doi.org/10.3390/app13095348.
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Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are {6.96[4.87,8.67], 12.49[7.96,14.23], 19.52[10.62,23.37], 28.79[12.96,34.57], 32.3[16.20,41.59], 28.8[15.13,37.18]} mg/dL, for prediction horizons (PH) of {15,30,60,120,180,240} min, respectively, while the baseline of the individually trained RMSE results are {6.37[5.07,6.70], 11.27[8.35,12.65], 17.44[11.08,20.93], 22.72[14.29,28.19], 28.45[14.79,34.38], 25.58[13.10,36.60]} mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability.
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Clingensmith, Christopher M., and Sabine Grunwald. "Predicting Soil Properties and Interpreting Vis-NIR Models from across Continental United States." Sensors 22, no. 9 (April 21, 2022): 3187. http://dx.doi.org/10.3390/s22093187.
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The United States NRCS has a soil database that has data collected from across the country over the last several decades. This also includes soil spectral scans. This data is available, but it may not be used to its full potential. For this study, pedon, horizon and spectral data was extracted from the database for samples collected from 2011 to 2015. Only sites that had been fully described and horizons that had been analyzed for the full suite of desired properties were used. This resulted in over 14,000 samples that were used for modeling and eight soil properties: soil organic carbon (SOC); total nitrogen (TN); total sulfur (TS); clay; sand; exchangeable calcium (Caex); cation exchange capacity (CEC); and pH. Four chemometric methods were employed for soil property prediction: partial least squares (PLSR); Random Forest (RF); Cubist; and multivariable adaptive regression splines (MARS). The dataset was sufficiently large that only random subsetting was used to create calibration (70%) and validation (30%) sets. SOC, TN, and TS had the strongest prediction results, with an R2 value of over 0.9. Caex, CEC and pH were predicted moderately well. Clay and sand models had slightly lower performance. Of the four methods, Cubist produced the strongest models, while PLSR produced the weakest. This may be due to the complex relationships between soil properties and spectra that PLSR could not capture. The only drawback of Cubist is the difficult interpretability of variable importance. Future research should include the use of environmental variables to improve prediction results. Future work may also avoid the use of PLSR when dealing with large datasets that cover large areas and have high degrees of variability.
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Pavani-Biju, Barbara, José G. Borges, Susete Marques, and Ana C. Teodoro. "Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging." Sustainability 16, no. 15 (July 26, 2024): 6423. http://dx.doi.org/10.3390/su16156423.
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Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data's spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available.
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Dill, Robert, Henryk Dobslaw, and Maik Thomas. "ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors." Artificial Satellites 58, no. 4 (December 1, 2023): 330–40. http://dx.doi.org/10.2478/arsa-2023-0013.
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Abstract Since more than 10 years, the Earth system modeling group at GFZ (ESMGFZ) provides effective angular momentum (EAM) functions for Earth orientation parameter assessment on a routinely daily basis. In addition to EAM of the individual Earth’s subsystems atmosphere, ocean, and hydrology, the global mass balance is calculated as barystatic sea level variation by solving explicitly the sea-level equation. ESMGFZ provides also 6-day forecasts for all of these EAM products. EAM forecasts are naturally degraded by forecast errors that typically grow with increasing forecast length, but they also show recurring patterns with occasionally higher errors at very short forecast horizons. To characterize such errors which are not randomly distributed in time, we divided the errors into a systematic and a stochastic contribution. In an earlier study, we were able to detect and remove the large systematic fraction occurring in the atmospheric angular momentum (AAM) wind term forecast errors with a cascading forward neural network model, thereby reducing the total forecast error by about 50%. In contrast, we were not able to remove the random error component assed in this study. Nevertheless, we show that machine learning methods are able to predict quasi-daily variations in time variable EAM forecasts error levels. We plan to provide these forecast error estimates along with the deterministic EAM forecast products for subsequent use in, for example, EOP Kalman filter prediction schemes.
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Ramspek, Chava L., Marie Evans, Christoph Wanner, Christiane Drechsler, Nicholas C. Chesnaye, Maciej Szymczak, Magdalena Krajewska, et al. "Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD." Journal of the American Society of Nephrology 32, no. 5 (March 8, 2021): 1174–86. http://dx.doi.org/10.1681/asn.2020071077.
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BackgroundVarious prediction models have been developed to predict the risk of kidney failure in patients with CKD. However, guideline-recommended models have yet to be compared head to head, their validation in patients with advanced CKD is lacking, and most do not account for competing risks.MethodsTo externally validate 11 existing models of kidney failure, taking the competing risk of death into account, we included patients with advanced CKD from two large cohorts: the European Quality Study (EQUAL), an ongoing European prospective, multicenter cohort study of older patients with advanced CKD, and the Swedish Renal Registry (SRR), an ongoing registry of nephrology-referred patients with CKD in Sweden. The outcome of the models was kidney failure (defined as RRT-treated ESKD). We assessed model performance with discrimination and calibration.ResultsThe study included 1580 patients from EQUAL and 13,489 patients from SRR. The average c statistic over the 11 validated models was 0.74 in EQUAL and 0.80 in SRR, compared with 0.89 in previous validations. Most models with longer prediction horizons overestimated the risk of kidney failure considerably. The 5-year Kidney Failure Risk Equation (KFRE) overpredicted risk by 10%–18%. The four- and eight-variable 2-year KFRE and the 4-year Grams model showed excellent calibration and good discrimination in both cohorts.ConclusionsSome existing models can accurately predict kidney failure in patients with advanced CKD. KFRE performed well for a shorter time frame (2 years), despite not accounting for competing events. Models predicting over a longer time frame (5 years) overestimated risk because of the competing risk of death. The Grams model, which accounts for the latter, is suitable for longer-term predictions (4 years).
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Beauchemin, S., R. R. Simard, M. A. Bolinder, M. C. Nolin, and D. Cluis. "Prediction of phosphorus concentration in tile-drainage water from the Montreal Lowlands soils." Canadian Journal of Soil Science 83, no. 1 (February 1, 2003): 73–87. http://dx.doi.org/10.4141/s02-029.
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Subsurface drainage systems can be a significant pathway for P transfer from some soils to surface waters. The objective of the study was to determine P concentration in tile-drainage water and its relationship to P status in surface soils (A horizons) from an intensively cultivated area in the Montreal Lowlands. The profiles of 43 soil units were characterized for their P contents and pedogenic properties. Tile-drainage water P concentrations were monitored over a 3-y r period on a weekly basis on 10 soil units, and four times during each growing season for the other 33 units. The soil units were grouped into lower and higher P sorbing soils using multiple discriminant equations developed in an earlier related study. The A horizons of the lower P sorbing soils had an elevated P saturation degree [mean Mehlich(III) P/Al = 17%] associated with total P concentrations in tile-drainage water consistently greater than the surface water quality standard of 0.03 mg total P L-1. Conversely, low P concentrations in tile-drainage waters (< 0.03 mg L-1) and a moderate mean Mehlich(III) P/Al ratio of 8% were observed in the higher P sorbing soil group. Total P concentrations in drainage systems were significantly related to soil P status in surface soils. Grouping soils according to their P sorption capacities increased the power of prediction based on only one soil variable. However, accurate predictions in terms of drain P concentration can hardly be obtained unless large dataset and other factors related to field management practices and hydrology of the sites are also considered. Therefore, a better alternative to predict the risk of P leaching is to work in terms of risk classes and rely on a multiple factor index. Key words: Tile-drainage water, phosphorus, P transfer, P loss, degree of soil P saturation, phosphorus index
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Amara-Ouali, Yvenn, Bachir Hamrouche, Guillaume Principato, and Yannig Goude. "Quantifying the Uncertainty of Electric Vehicle Charging with Probabilistic Load Forecasting." World Electric Vehicle Journal 16, no. 2 (February 9, 2025): 88. https://doi.org/10.3390/wevj16020088.
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The transition to electric vehicles (EVs) presents challenges and opportunities for the management of electrical networks. This paper focuses on developing and evaluating probabilistic forecasting algorithms to understand and predict EV charging behaviours, crucial for optimising grid operations and ensuring a balance between electricity demand and generation. Several forecasting approaches tailored to different time horizons are proposed across diverse model classes, including direct, bottom-up, and adaptive approaches. In all approaches, the target variable can be the load curve quantiles from 0.1 to 0.9 with 0.1 increments or prediction sets with a target coverage of 80%. Direct approaches learn from past load curves using GAMLSS or QGAM methods. Bottom-up approaches predict individual charging session characteristics (arrival time, charging duration, and energy demand) with mixture models before reconstructing the load curve. Adaptive approaches correct in real-time the prediction sets issued by direct or bottom-up approaches with conformal predictions. The experiments, conducted on real-world charging session data from Palo Alto, demonstrate the effectiveness of the proposed methods with regard to different metrics, including pinball loss, empirical coverage, and RPS. Overall, the results highlight the importance of quantifying uncertainty in load forecasts and the potential of probabilistic forecasting for EV load management.
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O'Connell, D. A., and P. J. Ryan. "Prediction of three key hydraulic properties in a soil survey of a small forested catchment." Soil Research 40, no. 2 (2002): 191. http://dx.doi.org/10.1071/sr01036.
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Direct measurement of ψ(θ) and K(θ) relationships at all observation sites in soil survey is not feasible. Three key hydraulic properties — water content at field capacity (θ–5 kPa), water content at wilting point (θ–1.5 MPa), and saturated hydraulic conductivity (Ks) — can be used to derive K(θ) and ψ(θ) when combined with bulk density. These properties were measured in 'calibration' horizons in a soil survey in Yambulla State Forest in south-east New South Wales. Pedotransfer functions (PTFs) for predicting θ-5 kPa, θ–1.5 MPa, and Ks from the physical and morphologic soil attributes are presented and evaluated here. Models for predicting θ–5 kPa and θ–1.5 MPa relied on per cent clay. An R2 of 0.64 (for θ–5 kPa) to 0.67 (for θ–1.5 MPa) was obtained for linear regressions using only morphologic explanatory variables. An R2 of 0.73 (for θ–5 kPa) to 0.90 (for θ–1.5 MPa) was obtained if laboratory-measured clay content was included as an explanatory variable. Ks was measured in situ using well permeameters, and used for developing PTFs. Large cores were taken from a small subsample of horizons and measurements of Ks, K–0.1 kPa, K–0.2 kPa, and K–0.5 kPa were made in the laboratory. Ks measurements from well permeameters were similar to K-0.5 kPa from laboratory measurements. Regression and tree models were used to predict Ks. The linear regression had an R2 of 0.55, while the tree models accounted for approximately 40% reduction in deviance. Bulk density was the most useful predictor in all Ks models. The inclusion of per cent rock fragments, bulk density, and estimated percentage clay as useful explanatory variables demonstrated the utility of functional descriptors not routinely measured in soil survey. The models are empirical and were locally calibrated for use in a soil survey. They may be applicable in target domains similar to the source domain (i.e. coarse-grained adamellite soils in similar climatic regimes). surrogates, saturated hydraulic conductivity, K(θ), ψ(θ), Ks, pedotransfer functions, soil survey, soil morphology, PTF.
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Park, Sophia, and Myeong Jun Kim. "Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach." Atmosphere 16, no. 3 (February 20, 2025): 239. https://doi.org/10.3390/atmos16030239.
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This study applied various machine learning techniques, including shrinkage methods, XGBoost, CSR, and random forest, to forecast ultrafine particulate matter (PM2.5) concentrations in Seoul, South Korea. The analysis incorporated key variables known to significantly influence PM2.5 levels, including meteorological data, coal-fired power generation, and PM2.5 concentrations in Dalian, China. Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. Out-of-sample multi-period forecasts were evaluated for each model using the RMSE, MAE, and Giacomini–White test to determine the most effective forecasting approach. It was found that the random forest model with the Boruta algorithm outperformed all other models, achieving improvements of 4% to 17% in the RMSE and 4% to 16.5% in the MAE across all forecast horizons. The results indicate that the random forest model and its variant incorporating the Boruta algorithm provided superior short-term forecasting performance. In particular, the Boruta algorithm highlighted the lagged variables of temperature, PM2.5 concentration, mean humidity, and Dalian PM2.5 concentration as critical factors for the accurate prediction of PM2.5 levels in Seoul. These findings underscore the utility of data-driven approaches to improve air quality forecasting and management.
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Hitziger, Martin, and Mareike Ließ. "Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes." Applied and Environmental Soil Science 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/809495.
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A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes).
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Zhang, Mengmeng, Guijun Han, Xiaobo Wu, Chaoliang Li, Qi Shao, Wei Li, Lige Cao, Xuan Wang, Wanqiu Dong, and Zenghua Ji. "SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea." Remote Sensing 16, no. 6 (March 14, 2024): 1034. http://dx.doi.org/10.3390/rs16061034.
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We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.
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Pañeda, Emilio Martínez. "Progress and opportunities in modelling environmentally assisted cracking." RILEM Technical Letters 6 (July 19, 2021): 70–77. http://dx.doi.org/10.21809/rilemtechlett.2021.145.
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Environmentally assisted cracking phenomena are widespread across the transport, defence, energy and construction sectors. However, predicting environmentally assisted fractures is a highly cross-disciplinary endeavour that requires resolving the multiple material-environment interactions taking place. In this manuscript, an overview is given of recent breakthroughs in the modelling of environmentally assisted cracking. The focus is on the opportunities created by two recent developments: phase field and multi-physics modelling. The possibilities enabled by the confluence of phase field methods and electro-chemo-mechanics modelling are discussed in the context of three environmental assisted cracking phenomena of particular engineering interest: hydrogen embrittlement, localised corrosion and corrosion fatigue. Mechanical processes such as deformation and fracture can be coupled with chemical phenomena like local reactions, ionic transport and hydrogen uptake and diffusion. Moreover, these can be combined with the prediction of an evolving interface, such as a growing pit or a crack, as dictated by a phase field variable that evolves based on thermodynamics and local kinetics. Suitable for both microstructural and continuum length scales, this new generation of simulation-based, multi-physics phase field models can open new modelling horizons and enable Virtual Testing in harmful environments.
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Bruzda, Joanna. "Does modal (auto)regression produce credible forecasts of macroeconomic indicators?" Wiadomości Statystyczne. The Polish Statistician 2024, no. 10 (October 31, 2024): 1–27. http://dx.doi.org/10.59139/ws.2024.10.1.
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Modal regression is a relatively new statistical technique that serves the purpose of modelling the modal value of a variable conditional on a set of explanatory variables. The aim of the study discussed in this paper is to assess the usefulness of modal regression in the analysis of economic time series and, more specifically, in short-term macroeconomic forecasting, on the example of industrial output indexes of 27 OECD countries. We focus on models of univariate time series and pose a question whether linear and nonlinear (threshold type) modal autoregression leads to more trustworthy macroeconomic forecasts than standard approaches based on modelling other measures of central tendency. The credibility of forecasts is understood as appropriately defined accuracy of point forecasts, and as narrow forecast intervals. Our empirical results demonstrate that linear modal autoregression can compete with other linearly-specified models both in terms of the aggregate forecast accuracy and the lengths of prediction intervals. It should be mentioned, however, that in the case of the study described in this paper, carried out on the data sample spanning the period from January 1994 to December 2019 and the period of forecasting allowing the determination of 100 forecasts in time horizon from one to four months according to the rolling schema, narrower prediction intervals in comparison with other estimation methods have turned out more likely for lower nominal confidence levels such as 80% and below, while differences in precision measurements, including credibility differences, have rarely proved statistically significant. Our analysis also shows that narrow-interval forecasts of economic growth rates might require specifying a GARCH equation, but on the other hand, at certain confidence levels and forecast horizons, models with GARCH equations might be outperformed in this respect by nonlinear (in mean or mode) autoregression. Another interesting fact is that modal autoregression minimises the robustified sMAPE indicators for one-step-ahead forecasts of the output indexes.
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Alekseev, Valery I. "Forecasting changes in the Earth’s climate system by instrumental measurements and paleodata in the phase-time region, consistent with changes in the barycentric motions of the SUN. Part 2." Yugra State University Bulletin 21, no. 1 (March 28, 2025): 48–62. https://doi.org/10.18822/byusu20250148-62.
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Subject of research is conditioned by the need to establish the root causes of climate change on the Earth and to predict changes in heliocosmic, climatic variables, natural disasters based on instrumental measurements and palaeodata. Purpose of research: application of the wavelet phase-time method of time series analysis to establish the strength of influence of the Sun’s barycentric motions on the variability of heliocosmic, climatic variables, natural disasters and fires, high-precision forecasting of variables in the phase-time domain for long horizons for hundreds of years by instrumental measurements and thousands of years by palaeodata. Methods and objects of research: wavelet phase method of analyses of time series of changes in heliocosmic and climatic variables, natural disasters and fires, curves of changes in climatic changes obtained by analyses of ice cores in the Antarctic and bottom sediments in the Atlantic. Main results of research. A method and algorithm for high-precision prediction of variables in the phase-time domain based on the use of wavelet transformation of one-dimensional plots of variable measurements over time into two-dimensional images over frequency (wavelet scale) and time with subsequent predictions of individual, selected frequency components of images of variables are obtained. It is assumed that at each frequency of the phase-frequency and time image the period of phase change in the prediction interval is equal to the average period of phase change in the observed time interval; at each selected frequency (wavelet scale) of the image section the coordinates of minima of the phase changing sawtooth in the observed time interval are estimated. The predicted variable curve is formed as the arithmetic mean of a set of predicted phase curves with opposite sign obtained for a set of selected frequencies in the image. Graphs of predicted changes in practically important heliocosmic, climatic variables, natural disasters, hurricanes, forest fires in Khanty-Mansiysk Autonomous Okrug – Yugra, number and areas of fires in Irkutsk Oblast, changes in the level of the Caspian Sea and the Amur River, where catastrophic river spills are observed, changes in the intensity of the warm Gulf Stream current were obtained. Consistency plots of changes in selected groups of variables in the observed and predicted time intervals by instrumental measurements were obtained and plotted. Graphs of observed and predicted climatic variables, orbital changes of the Earth in time interval -422÷300 thousand years and coordinated changes of characteristic groups of variables have been obtained; it has been obtained that modern global warming, displayed on the graphs of coordinated changes of some groups of variables, is a natural continuation of climate changes in the past and it will gradually, within 10-12 thousand years, be replaced by glaciation with subsequent warming and cooling in cyclic mode, as it was in the historical past.
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Jin, Yixuan. "Stock Price Analysis and Prediction Method Based on Machine Learning: Taking Apple Inc as an Example." Highlights in Business, Economics and Management 21 (December 12, 2023): 652–58. http://dx.doi.org/10.54097/hbem.v21i.14720.
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Stock forecasts are analyses of Apple's future performance based on financial data, market dynamics and macroeconomic factors. However, there are conflicting arguments that the wider the time horizon of the data, the more accurate the forecast. These forecasts are crucial for investment decisions, risk management and corporate governance. Therefore, in this paper, we will use vector autoregressive modelling to compare nine training sets with different time horizons and evaluate these nine sets of predictions by calculating the weights of the corresponding variables in the predictions. Knowledge of machine learning and graphical visualization is used to evaluate the share of five factors affecting stock prices as well as the training time horizon. This paper demonstrates that in the field of stock prediction the closer the time horizon is to the prediction the closer it is to the actual value. At the same time investors should consider multiple factors to diversify the risk.
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Alekseev, Valery I. "Forecasting changes in the earth’s climate system by instrumental measurements and paleodata in the phase-time region, consistent with changes in the barycentric motions of the sun. Part 1." Yugra State University Bulletin 20, no. 2 (October 10, 2024): 74–96. http://dx.doi.org/10.18822/byusu20240274-96.
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The relevance of the research is due to the need to establish the root cause of climate change on Earth and predict changes in heliocosmic, climatic variables, natural disasters by intramental measurements and paleodata, as time series, for long intervals of the time horizon in the phase domain, taking into account their cyclicity and interdependence of changes. Purpose of research: application of the time series analysis method of the time series to establish the strength of the influence of the barycentric movements of the Sun on the variability of heliocosmic, climatic variables, natural disasters and fires, high-precision prediction of variables in the phase-time domain for long horizons of hundreds and thousands of years by instrumental measurements and paleodata. Objects of research: time series of changes in heliocosmic and climatic variables, natural disasters and fires, curves of changes in climatic changes obtained by analysis of ice cores in Antarctica and bottom sediments in Antarctica. Methods of research: wavelet phase-frequency and phase-time analysis of images of heliocosmic and climatic variables, natural disasters and fires; calculation of the consistency of changes in selected groups on a set of phase-time characteristics of variables in a sliding mode. Main results of research: In the images of wavelet phase-time functions of many variables constructed from observations in 1600–2010, Jose periods of ~178 years were found, which are contained in changes in the characteristics of barycentric movements of the Sun, solar activity, solar constant, CO2,N2OEl Nino, solar wind, the level of the Caspian Sea, temperature in Greenland, the speed of rotation of the Earth, snow accumulation rates in the Indian Ocean sector of Antarctica, global temperature; cyclicity of changes in heliocosmic and climatic variables are consistent with changes in Baricentre; there is a significant difference in the consistency of changes in the phase-time characteristics of variables obtained at the southern hot and northern cold latitudes of the planet by about 2.1 times; in the northern part of the planet, changes in variables are more chaotic, due to the different influence of changes in the Baricentre variable, the magnetic fields of the Sun and the Earth on the variability of variables at different latitudes of the planet; significant variability and resonances of the phase characteristics of heliocosmic and climatic variables are observed by changes in Baricentre on the graphs of coordinated changes in groups of variables in the observed and predicted time intervals. The studies reveal multiple oscillatory responses of the Earth’s climate system to the impact of the Baricentre variable due to the heterogeneity of its components and the Earth’s magnetosphere in space. Changes in the set of phase-time characteristics of variables on the same graph in the observed and predicted time intervals in the interval ± π are displayed as changes in autowaves characteristic of self-organizing systems, characterizing climatic changes in environments in combination with the influence of the variable Baricentre.
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Wang, Meng, Changhe Niu, Zifan Wang, Yongxin Jiang, Jianming Jian, and Xiuying Tang. "Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery." Agriculture 14, no. 6 (May 24, 2024): 823. http://dx.doi.org/10.3390/agriculture14060823.
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To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.
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Ukalovic, D., B. Leeb, B. Rintelen, G. Eichbauer-Sturm, P. Spellitz, R. Puchner, M. Herold, et al. "POS0641 MACHINE LEARNING AND EXPLAINABLE AI METHODS CAN HELP TO PREDICT THE INEFFECTIVENESS OF INDIVIDUAL BIOLOGICAL DISEASE MODIFYING ANTIRHEUMATIC DRUGS (bDMARDS) – DATA FROM THE AUSTRIAN BIOLOGICAL REGISTRY BIOREG." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 597. http://dx.doi.org/10.1136/annrheumdis-2023-eular.3479.
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BackgroundThe continuous development of biological disease modifying antirheumatic drugs (bDMARDs) in recent years has significantly improved the treatment options for patients suffering from rheumatoid arthritis. Selecting the most effective biologic remains a challenge, since therapy-response is highly individual depending on the patient history, laboratory values and demographics.ObjectivesThe aim of this study was to investigate, if non-responders can be detected before therapy start using machine learning models and explainable artificial intelligence to provide a probability of non-response and to identify the most impactful contributing factors to the model output.MethodsData from the Austrian Registry for bDMARDs and tsDMARDs in Rheumatic Diseases – BIOREG were obtained. BIOREG provides a real-world data set, which covers rheumatology hospitals and practices throughout Austria. According to EULAR-guidelines the observation time window for treatment response is 6 months and the prediction time horizon was set at 6 months as well.Different machine learning models were trained for Abatacept (ABA), Adalimumab (ADA), Certolizumab (CERT), Etanercept (ETA) and Tocilizumab (TOC) to predict the risk of non-response per treat to target (ttt)-course. Nested cross-validation and hyperparameter tuning (iteration over fixed parameter grid) were applied. To evaluate the prognostic quality per model the area under the receiver operating characteristic (AUROC) was collected and the model with the highest score was selected for further evaluation. By applying the Explainable AI method SHAP (SHapley Additive exPlanations; a game-theoretic approach to evaluate variable importance) to each final model, the most contributing factors and direction of impact were evaluated.ResultsData from 1397 patients, 2004 (baseline) visits and 22 variables (19 after cleaning) with at least 100 ttt-courses per drug were included in the study.The best models per biologic achieved an AUROC-score of: CERT: 0.76 (95% CI, 0.67–0.86). TOC: 0.72 (95% CI, 0.69–0.79), ABA: 0.71 (95% CI, 0.65–0.77), ADA: 0.67 (95% CI, 0.62–0.76), ETA: 0.68 (95% CI, 0.53–0.85).The explainable AI interpreted visual analytic scores (VAS) as most important variables for ABA, ETA and TOC. High scores were associated with high risk of non-response for these drugs. For ADA, co-therapy with glucocorticoids was the most important and risk-increasing factor. For CERT, the dosage of the prescribed drug was ranked as the most influential variable; high dosages were associated with lower risk of non-response. Interestingly, some variables displayed opposite impacts in different drugs: Male gender was interpreted as risk-increasing for ABA and risk-decreasing for ETA. Moreover, negative rheumatoid-factor was interpreted as risk-decreasing for ABA/ETA, but risk-increasing for ADA/CERT.ConclusionThe results of our study show that non-responders of biological drugs can be detected with moderate to even good prognostic qualitybeforestarting a ttt-course, comparable to similar research with different prediction time horizons [1].The opposite impact of some variables in different bDMARDs as well as the difference in variable importance per bDMARD indicate, that selecting the right drug is highly dependent on the individual patient characteristic. Machine Learning could be of additional support for rheumatologists and patients by providing not only a prediction of ineffectiveness per drug, but also an explanation for the prediction.Reference[1]Koo, B.S., Eun, S., Shin, K. et al. Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics. Arthritis Res Ther 23, 178 (2021).https://doi.org/10.1186/s13075-021-02567-yAcknowledgements:NIL.Disclosure of InterestsDubravka Ukalovic Employee of: Siemens Healthineers. Siemens Healthineers is a medical technology company (NOT a pharmaceutical company), Burkhard Leeb Speakers bureau: AbbVie, Roche, MSD, Pfizer, Actiopharm, Boehringer-Ingelheim, Kwizda, Celgene, Sandoz, Grünenthal, Eli-Lilly, Consultant of: AbbVie, Amgen, Roche, MSD, Pfizer, Celgene, Grünenthal, Kwizda, Eli-Lilly, Novartis, Sandoz, Grant/research support from: TRB, Roche, Bernhard Rintelen Speakers bureau: BMS, Eli-Lilly, Pfizer, TRB-Chemedica, UCB, Wyeth, Consultant of: Abbott, Abbvie, Amgen, Gileat, Novartis, Pfizer, Roche, TRB-Chemedica, UCB, Wyeth, Grant/research support from: Abbott, Aesca, Amgen, Centocor, Eli-Lilly, Servier, UCB, Gabriela Eichbauer-Sturm Speakers bureau: AbbVie, Astro-Pharma, Grünenthal, Jansen, Eli-Lilly, Menarini, MSD, Novartis, Pfizer, Roche, TRB, UCB, Fresenius Kabi, Peter Spellitz: None declared, Rudolf Puchner Speakers bureau: AbbVie, BMS, Janssen, Kwizda, MSD, Pfizer, Celgene, Grünenthal, Eli-Lilly, Consultant of: AbbVie, Amgen, Pfizer, Celgene, Grünenthal, Eli-Lilly, Manfred Herold: None declared, Miriam Stetter: None declared, Vera Ferincz: None declared, Johannes Resch-Passini: None declared, Marcus Zimmermann-Rittereiser Employee of: Siemens Healthineers. Siemens Healthineers is a medical technology company (NOT a pharmaceutical company), Ruth Fritsch-Stork Speakers bureau: AbbVie, Astra Zeneca, Astropharm, Novartis.
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Luo, Yaneng, Handong Huang, Yadi Yang, Qixin Li, Sheng Zhang, and Jinwei Zhang. "Deepwater reservoir prediction using broadband seismic-driven impedance inversion and seismic sedimentology in the South China Sea." Interpretation 6, no. 4 (November 1, 2018): SO17—SO29. http://dx.doi.org/10.1190/int-2018-0029.1.
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In recent years, many important discoveries have been made in the marine deepwater hydrocarbon exploration in the South China Sea, which indicates the huge exploration potential of this area. However, the seismic prediction of deepwater reservoirs is very challenging because of the complex sedimentation, the ghost problem, and the low exploration level with sparse wells in deepwater areas. Conventional impedance inversion methods interpolate the low frequencies from well-log data with the constraints of interpreted horizons to fill in the frequency gap between the seismic velocity and seismic data and thereby recover the absolute impedance values that may be inaccurate and cause biased inversion results if wells are sparse and geology is complex. The variable-depth streamer seismic data contain the missing low frequencies and provide a new opportunity to remove the need to estimate the low-frequency components from well-log data. Therefore, we first developed a broadband seismic-driven impedance inversion approach using the seismic velocity as initial low-frequency model based on the Bayesian framework. The synthetic data example demonstrates that our broadband impedance inversion approach is of high resolution and it can automatically balance between the inversion resolution and stability. Then, we perform seismic sedimentology stratal slices on the broadband seismic data to analyze the depositional evolution history of the deepwater reservoirs. Finally, we combine the broadband amplitude stratal slices with the impedance inversion results to comprehensively predict the distribution of deepwater reservoirs. Real data application results in the South China Sea verify the feasibility and effectiveness of our method, which can provide a guidance for the future deepwater hydrocarbon exploration in this area.
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Akhmedov, T. R., and M. A. Aghayeva. "Prediction of petrophysical characteristics of deposits in Kurovdagh field by use of attribute analysis of 3D data." Geofizicheskiy Zhurnal 44, no. 3 (August 24, 2022): 103–12. http://dx.doi.org/10.24028/gj.v44i3.261976.
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The paper is devoted to predicting petrophysical parameters of productive series of Kurovdagh field by use of attribute analysis of seismic data to define the direction of reconnaissance works in this field. The paper considers geographical position of the study area, its cover by geological and geophysical studies and underlines, in particular, the importance of 3D seismic data acquisition for more detailed study of Kurovdagh structure. Lithological and stratigraphic characteristics of the section is also given in the paper with detailed description of Productive Series deposits. In addition, tectonics of the area is considered in more detail. It is noted that the area is attributed to the Low Kur basin — the compound of the large tectonic unit. The tectonic zone of the south-eastern Shirvan is embracing four anticlinal zones: Pirsaat-Khamamdagh; Kharami—Mishovdag—Kalmas—Khydirly—Aghaevir-Byandovan; Kursangya; Padar—Kurovdagh—Karabaghly—Babazanan—Duzdagh-Neftchala. The latter anticlinal zone is characterized by a significant length. In the north-western part between the folds of Padar and Karabaghly the brachyanticline of Kurovdagh is located. In the north it has the border with M. Kharami uplift, in the north-east with Mishovdagh fold, it borders with Kursyanga anticline in the south-east and in the south-west with wide Salyan trough. In the Near-Kur depression the existence of two tectonic stripes has been established. One of them embraces the south-eastern Shirvan, the other covers the eastern Mughan and the western portion of Salyan steppe. The detailed description of fold setting is given on the basis of 3D seismic survey data. It has been indicated that the results of 3D data interpretation made it possible to study in more detail and make changes in the scheme of faults location accepted earlier. The other problem considered in the paper is the oil-and-gas presence in Kurovdagh field, which is related to the Absheron stage of Pleistocene, Akchagyl stage and Productive series (horizons of PS01—PS20) of Neogene, with lithology represented by sandy-clay rocks with various degrees of calcareousness. The structure of each of indicated horizons is rather complicated and variable in lateral. The most complicated of them is the Middle Absheron sub-stage, with identified 11 oil-bearing layers. Study results are given in the end of the paper. For prediction purposes within the study area we have prepared normalized curves of relative parameter of SP-ASP, gamma-log — dGR and resistivity by use of well logging data. The analysis of dependence of seismic attributes on petrophysical parameters within target interval, identified the low information bearing ability of SP method and gamma-log across the study area and established a good correlation between resistivity curve and instantaneous amplitudes, frequencies and dip angles. The clay cubes have been designed. To outline productive layers, we have applied multidimensional filters with cut offs for reservoir and as a result we have acquired a cube for supposed distribution of productive layers. It is emphasized that the conducted studies led to the conclusion that due to the complexity and interference nature of the observed wave pattern in some parts of the Kurovdagh structure, it was not possible to reliably convert the attributes of the seismic wave field into petrophysical parameters.
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Lawson, John R., Corey K. Potvin, Patrick S. Skinner, and Anthony E. Reinhart. "The Vice and Virtue of Increased Horizontal Resolution in Ensemble Forecasts of Tornadic Thunderstorms in Low-CAPE, High-Shear Environments." Monthly Weather Review 149, no. 4 (April 2021): 921–44. http://dx.doi.org/10.1175/mwr-d-20-0281.1.
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AbstractTornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.
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González-Enrique, Javier, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso-López, Daniel Urda, Lipika Deka, and Ignacio J. Turias. "Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)." Sensors 21, no. 5 (March 4, 2021): 1770. http://dx.doi.org/10.3390/s21051770.
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This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.
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Abduljabbar, Rusul, Hussein Dia, and Sohani Liyanage. "Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information." Applied Sciences 14, no. 23 (November 27, 2024): 11047. http://dx.doi.org/10.3390/app142311047.
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This study addresses the challenges of predicting traffic flow on arterial roads where dynamic behaviours such as passenger pick-ups, vehicle turns, and parking interruptions complicate forecasting. The research develops and evaluates unidirectional and bidirectional Long Short-Term Memory (LSTM) models using a dataset of 70,072 observations collected over 12 months from Hoddle Street in Melbourne, Australia. The models were trained to predict traffic speeds and volumes up to 60 min ahead. The results indicated that the BiLSTM model significantly outperformed unidirectional LSTM, achieving over 99% accuracy for speed predictions and over 96% for volume predictions. The research also tested the impacts of incorporating weather variables such as rainfall, temperature, humidity, and wind speed on model performance, which was found to provide small improvements. Traffic flow prediction accuracy increased from 97.5% to 97.6% for 30-min horizons, and from 96.9% to 97.6% for 60-min horizons. Although the inclusion of weather data only marginally enhanced prediction performance, its inclusion has practical implications for public awareness of travel impacts under severe weather. The findings in this study highlight the effectiveness of deep learning techniques for traffic forecasting on arterial roads, establishing a foundation for future research in this area.
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Bergeron, Jean M., Mélanie Trudel, and Robert Leconte. "Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions." Hydrology and Earth System Sciences 20, no. 10 (October 28, 2016): 4375–89. http://dx.doi.org/10.5194/hess-20-4375-2016.
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Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.
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Wentz, Victor Hugo, Joylan Nunes Maciel, Jorge Javier Gimenez Ledesma, and Oswaldo Hideo Ando Junior. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models." Energies 15, no. 7 (March 27, 2022): 2457. http://dx.doi.org/10.3390/en15072457.
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The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p < 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.
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Mishra, Pradeep, Khder Alakkari, Mostafa Abotaleb, Pankaj Kumar Singh, Shilpi Singh, Monika Ray, Soumitra Sankar Das, et al. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)." Data 6, no. 11 (November 2, 2021): 113. http://dx.doi.org/10.3390/data6110113.
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Economics suffers from a blurred view of the economy due to the delay in the official publication of macroeconomic variables and, essentially, of the most important variable of real GDP. Therefore, this paper aimed at nowcasting GDP in India based on high-frequency data released early. Instead of using a large set of data thus increasing statistical complexity, two main indicators of the Indian economy (economic policy uncertainty and consumer price index) were relied on. The paper followed the MIDAS–Almon (PDL) weighting approach, which allowed us to successfully capture structural breaks and predict Indian GDP for the second quarter of 2021, after evaluating the accuracy of the nowcasting and out-of-sample prediction. Our results indicated low values of the RMSE in the sample and when predicting the out-of-sample1- and 4-quarter horizon, but RMSE increased when predicting the 10-quarter horizon. Due to the effect of the short-term structural break, we found that RMSE values decreased for the last prediction point.
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Lopes, Gustavo. "The wisdom of crowds in forecasting at high-frequency for multiple time horizons: A case study of the Brazilian retail sales." Brazilian Review of Finance 20, no. 2 (June 19, 2022): 77–115. http://dx.doi.org/10.12660/rbfin.v20n2.2022.85016.
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This case study compares the forecasting accuracy obtained for four daily Brazilian retail sales indexes at four time prediction horizons. The performance of traditional time series forecasting models, artificial neural network architectures and machine learning algorithms were compared in other to evaluate the existence of a single best performing model. Afterwards, ensemble methods were added to model comparison to verify if accuracy improvement could be obtained. Evidence found in this case study suggests that a consistent forecasting strategy exists for the Brazilian retail indexes by applying both seasonality treatment for holidays and calendar effects and by using an ensemble method which main inputs are the predictions of all models with calendar variables. This strategy was consistent across all 16 index and time horizon combinations since ensemble methods either outperformed the best single models or there were no statistical difference from them in a Diebold-Mariano's test.
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Gong, Chen Chris, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer. "Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2." Geoscientific Model Development 16, no. 17 (August 31, 2023): 4977–5033. http://dx.doi.org/10.5194/gmd-16-4977-2023.
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Abstract. Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the temporal and spatial details required to properly analyze the key role of variable renewable energy (VRE) in decarbonizing the power sector and enabling emission reductions through end-use electrification. In contrast, power sector models (PSMs) can incorporate high spatiotemporal resolutions but tend to have narrower sectoral and geographic scopes and shorter time horizons. To overcome these limitations, here we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a long-term IAM and a detailed PSM. The key innovation is that the framework uses the market values of power generations and the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REgional Model of INvestments and Development (REMIND) v3.0.0 and the PSM Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2 and confirm the theoretical prediction of almost-full convergence in terms of both decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is < 5 % for a simple configuration (no storage, no flexible demand) under a “proof-of-concept” baseline scenario and 6 %–7 % for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. The remaining differences in the two models can be explained by a slight mismatch between the standing capacities in the real world and optimal modeling solutions based purely on cost competition. Since our approach is based on fundamental economic principles, it is also applicable to other IAM–PSM pairs.
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McKenzie, Neil, and David Jacquier. "Improving the field estimation of saturated hydraulic conductivity in soil survey." Soil Research 35, no. 4 (1997): 803. http://dx.doi.org/10.1071/s96093.
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Prediction of the movement and storage of water in soil is central to quantitative land evaluation. However, spatial and temporal predictions have not been provided by most Australian soil surveys. The saturated hydraulic conductivity (Ks) is an essential parameter for description of water movement in soil and its estimation has been considered too difficult for logistic and technical reasons. The Ks cannot be measured everywhere and relationships with readily observed morphological variables have to be established. However, conventional morphology by itself is a poor predictor of Ks. We have developed a more functional set of morphological descriptors better suited to the prediction of Ks. The descriptors can be applied at several levels of detail. Measurements of functional morphology and Ks were made on 99 horizons from 36 sites across south-eastern Australia. Useful predictions of Ks were possible using field texture, grade of structure, areal porosity, bulk density, dispersion index, and horizon type. A simple visual estimate of areal porosity was satisfactory, although a more quantitative system of measurement provided only slightly better predictions. Regression trees gave more plausible predictive models than standard multiple regressions because they provided a realistic portrayal of the non-additive and conditional nature of the relationships between morphology and Ks. The results are encouraging and indicate that coarse-level prediction of Ks is possible in routine soil survey. Direct measurement of Ks does not appear to be generally feasible because of the high cost, dynamic nature of Ks, and substantial short-range variation in the field. Prediction is further constrained by the limited returns from more sophisticated morphological predictors. The degree to which this limits practical land evaluation is yet to be demonstrated.
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Kerry, Colette Gabrielle, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza. "Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system." Geoscientific Model Development 17, no. 6 (March 22, 2024): 2359–86. http://dx.doi.org/10.5194/gmd-17-2359-2024.
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Abstract. Ocean models must be regularly updated through the assimilation of observations (data assimilation) in order to correctly represent the timing and locations of eddies. Since initial conditions play an important role in the quality of short-term ocean forecasts, an effective data assimilation scheme to produce accurate state estimates is key to improving prediction. Western boundary current regions, such as the East Australia Current system, are highly variable regions, making them particularly challenging to model and predict. This study assesses the performance of two ocean data assimilation systems in the East Australian Current system over a 2-year period. We compare the time-dependent 4-dimensional variational (4D-Var) data assimilation system with the more computationally efficient, time-independent ensemble optimal interpolation (EnOI) system, across a common modelling and observational framework. Both systems assimilate the same observations: satellite-derived sea surface height, sea surface temperature, vertical profiles of temperature and salinity (from Argo floats), and temperature profiles from expendable bathythermographs. We analyse both systems' performance against independent data that are withheld, allowing a thorough analysis of system performance. The 4D-Var system is 25 times more expensive but outperforms the EnOI system against both assimilated and independent observations at the surface and subsurface. For forecast horizons of 5 d, root-mean-squared forecast errors are 20 %–60 % higher for the EnOI system compared to the 4D-Var system. The 4D-Var system, which assimilates observations over 5 d windows, provides a smoother transition from the end of the forecast to the subsequent analysis field. The EnOI system displays elevated low-frequency (>1 d) surface-intensified variability in temperature and elevated kinetic energy at length scales less than 100 km at the beginning of the forecast windows. The 4D-Var system displays elevated energy in the near-inertial range throughout the water column, with the wavenumber kinetic energy spectra remaining unchanged upon assimilation. Overall, this comparison shows quantitatively that the 4D-Var system results in improved predictability as the analysis provides a smoother and more dynamically balanced fit between the observations and the model's time-evolving flow. This advocates the use of advanced, time-dependent data assimilation methods, particularly for highly variable oceanic regions, and motivates future work into further improving data assimilation schemes.
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El Ghazouli, Khalid, Jamal El Khattabi, Isam Shahrour, and Aziz Soulhi. "Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network." H2Open Journal 4, no. 1 (January 1, 2021): 276–90. http://dx.doi.org/10.2166/h2oj.2021.107.
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Abstract Wastewater flow forecasts are key components in the short- and long-term management of sewer systems. Forecasting flows in sewer networks constitutes a considerable uncertainty for operators due to the nonlinear relationship between causal variables and wastewater flows. This work aimed to fill the gaps in the wastewater flow forecasting research by proposing a novel wastewater flow forecasting model (WWFFM) based on the nonlinear autoregressive with exogenous inputs neural network, real-time, and forecasted water consumption with an application to the sewer system of Casablanca in Morocco. Furthermore, this research compared the two approaches of the forecasting model. The first approach consists of forecasting wastewater flows on the basis of real-time water consumption and infiltration flows, and the second approach considers the same input in addition to water distribution flow forecasts. The results indicate that both approaches show accurate and similar performances in predicting wastewater flows, while the forecasting horizon does not exceed the watershed lag time. For prediction horizons that exceed the lag time value, the WWFFM with water distribution forecasts provided more reliable forecasts for long-time horizons. The proposed WWFFM could benefit operators by providing valuable input data for predictive models to enhance sewer system efficiency.
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Dumm, Gabriel, Lauren Fins, Russell T. Graham, and Theresa B. Jain. "Distribution of Fine Roots of Ponderosa Pine and Douglas-Fir in a Central Idaho Forest." Western Journal of Applied Forestry 23, no. 4 (October 1, 2008): 202–5. http://dx.doi.org/10.1093/wjaf/23.4.202.
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Abstract This study describes soil horizon depth and fine root distribution in cores collected at two distances from the boles of Douglas-fir and ponderosa pine trees at a study site in a central Idaho forest. Concentration and content of fine roots extracted from soil cores were compared among species, soil horizons, tree size, and distance from bole. Approximately 80% of litter and humus samples contained no fine roots. The highest fine root content and concentrations of fine roots occurred in deep mineral soil for both species (1.24 g and 2.82 g/l for Douglas-fir and 0.98g and 2.24 g/l for ponderosa pine, respectively). No statistically significant differences were found in fine root content or concentration between species in any of the four soil horizons. Tree size was not a significant factor in fine root distribution in this study. Significant variables were horizon, distance from bole, and interactions among tree size, location of sample, and soil horizon. This study, which was part of a larger US Forest Service study to develop a predictive model of postfire tree mortality, provides baseline information that may be useful in predicting postfire damage to fine roots.
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Aler, Ricardo, Javier Huertas-Tato, José M. Valls, and Inés M. Galván. "Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach." Energies 12, no. 24 (December 10, 2019): 4713. http://dx.doi.org/10.3390/en12244713.
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Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizons.
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Mendonça de Paiva, Gabriel, Sergio Pires Pimentel, Bernardo Pinheiro Alvarenga, Enes Gonçalves Marra, Marco Mussetta, and Sonia Leva. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks." Energies 13, no. 11 (June 11, 2020): 3005. http://dx.doi.org/10.3390/en13113005.
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The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.
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Carreno-Madinabeitia, Sheila, Gabriel Ibarra-Berastegi, Jon Sáenz, Eduardo Zorita, and Alain Ulazia. "Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)." Atmosphere 11, no. 1 (December 29, 2019): 45. http://dx.doi.org/10.3390/atmos11010045.
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This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.
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He, Hongwen, Jianfei Cao, and Jiankun Peng. "Online Prediction with Variable Horizon for Vehicle's Future Driving-Cycle." Energy Procedia 105 (May 2017): 2348–53. http://dx.doi.org/10.1016/j.egypro.2017.03.674.
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Cao, Jianfei, Jiankun Peng, and Hongwen He. "Research on Model Prediction Energy Management Strategy with Variable Horizon." Energy Procedia 105 (May 2017): 3565–70. http://dx.doi.org/10.1016/j.egypro.2017.03.819.
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