To see the other types of publications on this topic, follow the link: Streamflow – Swaziland – Mathematical models.
Journal articles on the topic 'Streamflow – Swaziland – Mathematical models'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 16 journal articles for your research on the topic 'Streamflow – Swaziland – Mathematical models.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
SIQUEIRA, HUGO, LEVY BOCCATO, ROMIS ATTUX, and CHRISTIANO LYRA. "UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING." International Journal of Neural Systems 24, no. 03 (February 19, 2014): 1430009. http://dx.doi.org/10.1142/s0129065714300095.
Full textAbstract:
Modern unorganized machines — extreme learning machines and echo state networks — provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.
2
Rezaie-Balf, Mohammad, and Ozgur Kisi. "New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine." Hydrology Research 49, no. 3 (March 27, 2017): 939–53. http://dx.doi.org/10.2166/nh.2017.283.
Full textAbstract:
Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.
3
Mazzoleni, M., M. Verlaan, L. Alfonso, M. Monego, D. Norbiato, M. Ferri, and D. P. Solomatine. "Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?" Hydrology and Earth System Sciences Discussions 12, no. 11 (November 3, 2015): 11371–419. http://dx.doi.org/10.5194/hessd-12-11371-2015.
Full textAbstract:
Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash–Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.
4
Li, Yujie, Zhongmin Liang, Yiming Hu, Binquan Li, Bin Xu, and Dong Wang. "A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy." Journal of Hydroinformatics 22, no. 2 (November 7, 2019): 310–26. http://dx.doi.org/10.2166/hydro.2019.066.
Full textAbstract:
Abstract In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.
5
Li, Xue, Jian Sha, You-meng Li, and Zhong-Liang Wang. "Comparison of hybrid models for daily streamflow prediction in a forested basin." Journal of Hydroinformatics 20, no. 1 (November 29, 2017): 191–205. http://dx.doi.org/10.2166/hydro.2017.189.
Full textAbstract:
Abstract Accurate forecasting of daily streamflow is essential for water resource planning and management. As a typical non-stationary time series, it is difficult to avoid the effects of noise in the hydrological data. In this study, the wavelet threshold de-noising method was applied to pre-process daily flow data from a small forested basin. The key factors influencing the de-noising results, such as the mother wavelet type, decomposition level, and threshold functions, were examined and determined according to the signal to noise ratio and mean square error. Then, three mathematical techniques, including an optimized back-propagation neural network (BPNN), optimized support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were used to predict the daily streamflow based on raw data and wavelet de-noising data. The performance of the three models indicated that a wavelet de-noised time series could improve the forecasting accuracy. The SVR showed a better overall performance than BPNN and ANFIS during both the training and validating periods. However, the estimation of low flow and peak flow indicated that ANFIS performed best in the prediction of low flow and that SVR was slightly superior to the others for forecasting peak flow.
6
Samadi, S. Zahra. "Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States." Hydrology Research 48, no. 2 (July 4, 2016): 395–415. http://dx.doi.org/10.2166/nh.2016.034.
Full textAbstract:
One of the key inputs of a hydrologic budget is the potential evapotranspiration (PET), which represents the hypothetical upper limit to evapotranspirative water losses. However, different mathematical formulas proposed for defining PET often produce inconsistent results and challenge hydrological estimation. The objective of this study is to investigate the effects of the Priestley–Taylor (P–T), Hargreaves, and Penman–Monteith methods on daily streamflow simulation using the Soil and Water Assessment Tool (SWAT) for the southeastern United States. PET models are compared in terms of their sensitivity to the SWAT parameters and their ability to simulate daily streamflow over a five-year simulation period. The SWAT model forced by these three PET methods and by gauged climatic dataset showed more deficiency during low and peak flow estimates. Sensitive parameters vary in magnitudes with more skew and bias in saturated soil hydraulic conductivity and shallow aquifer properties. The results indicated that streamflow simulation using the P–T method performed well especially during extreme events’ simulation.
7
Lambert, M. F., J. P. Whiting, and A. V. Metcalfe. "A non-parametric hidden Markov model for climate state identification." Hydrology and Earth System Sciences 7, no. 5 (October 31, 2003): 652–67. http://dx.doi.org/10.5194/hess-7-652-2003.
Full textAbstract:
Abstract. Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions
8
Koycegiz, Cihangir, Meral Buyukyildiz, and Serife Yurdagul Kumcu. "Spatio-temporal analysis of sediment yield with a physically based model for a data-scarce headwater in Konya Closed Basin, Turkey." Water Supply 21, no. 4 (January 19, 2021): 1752–63. http://dx.doi.org/10.2166/ws.2021.016.
Full textAbstract:
Abstract There are many empirical, semi-empirical and mathematical methods that have been developed to estimate sediment yield by researchers. In the last decades, the advancement in computer technologies has increased the use of mathematical models as they can solve the system more rapidly and accurately. The Soil and Water Assessment Tool (SWAT) is one of the physically based hydrological models that is preferred to compute sediment yield. In this study, spatial and temporal analysis of sediment yield in the Çarşamba Stream located at the Konya Closed Basin has been investigated using the SWAT model. Streamflow and sediment data collected during the 2003–2015 time period have been used in the analysis. Consequently, the SWAT presented satisfactory results compared with R2 = 0.68, Nash–Sutcliffe Efficiency (NSE) = 0.68 in calibration and R2 = 0.76, NSE = 0.66 in validation. According to the model results, spatial asymmetry in terms of sediment yield was determined in the sub-basins of the study area.
9
Mazzoleni, Maurizio, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine. "Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?" Hydrology and Earth System Sciences 21, no. 2 (February 14, 2017): 839–61. http://dx.doi.org/10.5194/hess-21-839-2017.
Full textAbstract:
Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.
10
Tran, Vinh Ngoc, and Jongho Kim. "Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion." Water 13, no. 2 (January 15, 2021): 203. http://dx.doi.org/10.3390/w13020203.
Full textAbstract:
Reliable hydrologic models are essential for planning, designing, and management of water resources. However, predictions by hydrological models are prone to errors due to a variety of sources of uncertainty. More accurate quantification of these uncertainties using a large number of ensembles and model runs is hampered by the high computational burden. In this study, we developed a highly efficient surrogate model constructed by sparse polynomial chaos expansion (SPCE) coupled with the least angle regression method, which enables efficient uncertainty quantifications. Polynomial chaos expansion was employed to surrogate a storage function-based hydrological model (SFM) for nine streamflow events in the Hongcheon watershed of South Korea. The efficiency of SPCE is investigated by comparing it with another surrogate model, full polynomial chaos expansion (FPCE) built by a well-known, ordinary least square regression (OLS) method. This study confirms that (1) the performance of SPCE is superior to that of FPCE because SPCE can build a more accurate surrogate model (i.e., smaller leave-one-out cross-validation error) with one-quarter the size (i.e., 500 versus 2000). (2) SPCE can sufficiently capture the uncertainty of the streamflow, which is comparable to that of SFM. (3) Sensitivity analysis attained through visual inspection and mathematical computation of the Sobol’ index has been of great success for SPCE to capture the parameter sensitivity of SFM, identifying four parameters, α, Kbas, Pbas, and Pchn, that are most sensitive to the likelihood function, Nash-Sutcliffe efficiency. (4) The computational power of SPCE is about 200 times faster than that of SFM and about four times faster than that of FPCE. The SPCE approach builds a surrogate model quickly and robustly with a more compact experimental design compared to FPCE. Ultimately, it will benefit ensemble streamflow forecasting studies, which must provide information and alerts in real time.
11
De OLIVEIRA, Guilherme Garcia, Dejanira Luderitz SALDANHA, and Laurindo Antonio GUASSELLI. "MODELS FOR SPATIALIZATION AND FORECASTING OF FLOODED AREAS IN THE SÃO SEBASTIÃO DO CAÍ URBAN ZONE, RIO GRANDE DO SUL STATE, BRAZIL." Pesquisas em Geociências 38, no. 2 (August 31, 2011): 132. http://dx.doi.org/10.22456/1807-9806.26379.
Full textAbstract:
The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.
12
Dibike, Yonas B., and Paulin Coulibaly. "TDNN with logical values for hydrologic modeling in a cold and snowy climate." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 289–300. http://dx.doi.org/10.2166/hydro.2008.049.
Full textAbstract:
Watershed runoff in areas with heavy seasonal snow cover is usually estimated using physically based conceptual hydrologic models. Such simulation models normally require a snowmelt algorithm consisting of a surface energy balance and some accounting of internal snowpack processes to be part of the modeling system. On the other hand, artificial neural networks are flexible mathematical structures that are capable of identifying such complex nonlinear relationships between input and output datasets from historical precipitation, temperature and streamflow records. This paper presents the findings of a study on using a form of time-delayed neural network, namely time-lagged feedforward neural network (TLFN), that implicitly accounts for snow accumulation and snowmelt processes through the use of logical values and tapped delay lines. The logical values (in the form of symbolic inputs) are used to implicitly include seasonal information in the TLFN model. The proposed method has been successfully applied for improved precipitation–runoff modeling of both the Chute-du-Diable reservoir inflows and the Serpent River flows in northeastern Canada where river flows and reservoir inflows are highly influenced by seasonal snowmelt effects. The study demonstrates that the TLFN with logical values is capable of modeling the precipitation–runoff process in a cold and snowy climate by relying on ‘logical input values’ and tapped delay lines to implicitly recognize the temporal input–output patterns in the historical data. The study results also show that, once the appropriate input patterns are identified, the time-lagged neural network based models performed quite well, especially for spring peak flows, and demonstrated comparable performance in simulating the precipitation–runoff processes to that of a physically based hydrological model, namely HBV.
13
Abdulai, Patricia Jitta, and Eun-Sung Chung. "Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method." Sustainability 11, no. 16 (August 8, 2019): 4283. http://dx.doi.org/10.3390/su11164283.
Full textAbstract:
The consequence of climate variations on hydrology remains the greatest challenging aspect of managing water resources. This research focused on the quantitative approach of the uncertainty in variations of climate influence on drought pattern of the Cheongmicheon watershed by assigning weights to General Circulation Models (GCMs) based on model performances. Three drought indices, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are used for three durations 3-, 6- and 9-months. This study included 27 GCMs from Coupled Model Intercomparison Project 5 (CMIP5) and considered three future periods (2011–2040, 2041–2070 and 2071–2100) of the concentration scenario of Representation Concentration Pathway (RCP) 4.5. Compared to SPEI and SDI, SPI identified more droughts in severe or extreme categories of shorter time scales than SPEI or SDI. The results suggested that the discrepancy in temperature plays a significant part in characterizing droughts. The Reliability Ensemble Averaging (REA) technique was used to give a mathematical approximation of associated uncertainty range and reliability of future climate change predictions. The uncertainty range and reliability of Root Mean Square Error (RMSE) varied among GCMs and total uncertainty ranges were between 50% and 200%. This study provides the approach for realistic projections by incorporating model performance ensemble averaging based on weights from RMSE.
14
"Intercomparison of process-based physical and mathematical models in data-scarce semi-arid region of Eritrea." Water sector of Russia: problems, technologies, management, no. 1, 2021 (2021): 86–112. http://dx.doi.org/10.35567/1999-4508-2021-1-6.
Full textAbstract:
Watershed models simulate natural hydrological and biogeochemical processes within watersheds as well as quantify the impact of human activities on these processes. Among them, rainfall-runoff models have been widely applied for generating hydrological responses using reanalysis datasets as forcing variables in data-scare regions. In the present study, Soil and Water Assessment Tool model and rainfall-runoff model were employed to simulate streamflow from a small watershed with arid and semi-arid climate. As such, models that provide reliable streamflow predictions in the region as well as whose errors and uncertainties are within acceptable ranges could be identified. The intercomparison of the models’ performances indicated that the Soil and Water Assessment Tool model relatively outperformed the rainfall-runoff model. However, while most of the statistical evaluations proved an acceptable performance of the Soil and Water Assessment Tool model, significant amounts of uncertainties during calibration and validation procedures were noticed. Among the possible sources of errors, errors due to forcing variables were highly likely to be responsible for unsatisfactory performances of the selected models. In this regard, to minimize model uncertainty and thereupon improve its performance, ground-based data collection need to be boosted up. Besides, the study highlighted the need for further investigation on the possible mechanisms of properly applying reanalysis datasets in arid and semi-arid regions.
15
Bhatt, Ankit, and Ajay Pradhan. "Uncertainty of Streamflow Forecasting with the Climate Change Scenario in India." Journal of Water Engineering and Management 1, no. 3 (2020). http://dx.doi.org/10.47884/jweam.v1i3pp01-06.
Full textAbstract:
Streamflow and rainfall estimates have utmost importance to compute detailed water availability and hydrology for many sectors such as agriculture, water management, and food security. There are various models developed over the years for runoff estimation but among them only a few models incorporate climate change factors. Snowmelt and rainfall are the main sources of surface as well as groundwater resource and the main inputs in runoff models for estimation of streamflow. There are numerous factors which leads to climate change which intern affects the distribution on rainfall on spatial and temporal scales and the rate of melting of snows in the Himalayan region. Uncertainties in projected changes in the hydrological systems arise from internal variability in the climatic system, uncertainty about future greenhouse gas and aerosol emissions, the translations of these emissions into climate change by global climate models, and hydrological model uncertainty. Projections become less consistent between models as the spatial scale decreases. The uncertainty of climate model projections for freshwater assessments is often taken into account by using multi-model ensembles. The multi-model ensemble approach is, however, not a guarantee of reducing uncertainty in mathematical models. In recent years the floods have occurred due to high intensity rainfall occurred in a very short time, but in several cases the flooding has also occurred because the rainfall has fallen at times when all the storage systems have not been emptied after the previous rainfall. This is what we call coupled rainfall. There is currently no recommendation for how to take coupled rainfall account when applying the climate change scenario. It is estimated that such changes represent at a large scale, and cannot be applied to shorter temporal and smaller spatial scales. In areas where rainfall and runoff are very low (e.g., desert areas), small changes in runoff can lead to large percentage changes. In some regions, the sign of projected changes in runoff differs from recently observed trends. Moreover, in some areas with projected increases in runoff, different seasonal effects are expected, such as increased wet season runoff and decreased dry season runoff. Studies using results from fewer climate models can be considerably different from the other models
16
Panant, Charoen, and Samakkee Boonyawat. "A Simplified Rainfall-Streamflow Network Model on Multivariate Regression Analysis for Water Level Forecasting in Klong Luang (KGT.19 Station) Sub-watershed, Chon Buri Province, Thailand." Applied Environmental Research, October 17, 2014, 53–65. http://dx.doi.org/10.35762/aer.2014.36.4.6.
Full textAbstract:
A simplifiedrainfall-streamflownetwork model based on multivariate linear regression (MLR) analysishas been proposed. To determine significant coefficients of streamflow network, eleven MLR models were examined. The study’s three objectives were 1) to develop a novel a mathematical model based on MLR analysis for forecasting optimal water levels;2) to determine the most significant coefficient of rainfall-streamflow network among in the area of interest in the vicinity of Klong Luang sub-watershedKGT.19 station; and3) to apply the optimal MLR model forwater level andflood forecasting mapsin Klong Luang Sub-watershed. We used Geographic Information System (GIS) and Remotely Sensed Data (RS) data recorded from Klong Luang (KGT.19 Station) sub-watershed, and Phanat Nikhom, Chonburi, Ban Bueng and Phan Thong districts, in Chonburi Province, Thailand.The findings indicated that the MLR based Model No. 8 is the most applicable and effective. The proposed model also could be applied in water level forecasting, water resource management, flood hazard planning, and flood early warning.