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Waghmare, Dr Vivek. "Machine Learning Technique for Rainfall Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 594–600. http://dx.doi.org/10.22214/ijraset.2021.35032.
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Rain prediction is one of the most challenging and uncertain tasks that has a profound effect on human society. Timely and accurate forecasting can help significantly reduce population and financial losses. This study presents a collection of tests involving the use of conventional machine learning techniques to create rainfall prediction models depending on the weather information of the area. This Comparative research was conducted focusing on three aspects: modeling inputs, modeling methods, and prioritization techniques. The results provide a comparison of the various test metrics for these machine learning methods and their reliability estimates in rain by analyzing weather data. This study seeks a unique and effective machine learning system for predicting rainfall. The study experimented with different parameters of the rainfall from various regions in order to assess the efficiency and durability of the model. The machine learning model is focused on this study. Rainfall patterns in this study are collected, trained and tested for achievement of sustainable outcomes using machine learning models. The monthly rainfall predictions obtained after training and testing are then compared to real data to ensure the accuracy of the model The results of this study indicate that the model has been successful in it predicting monthly rain data and specific parameters.
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Wakure, Govind, Rahul Lotlikar, Pushant Mangilipelli, and Saif Khan. "Rainfall Prediction Model Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 939–44. http://dx.doi.org/10.22214/ijraset.2022.42244.
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Abstract: Predicting rainfall is one of the most difficult aspects of weather forecasting. Accurate and timely rainfall forecasting can be extremely useful in preparing for ongoing building projects, transportation activities, agricultural jobs, aviation operations, and flood situations, among other things. By finding hidden patterns from available elements of past meteorological data, machine learning techniques may accurately predict rainfall. This study adds to the body of knowledge by giving a comprehensive examination and assessment of the most recent Machine Learning algorithms for rainfall prediction. This study looked at publications that were published between 2013 and 2017 and were found in reputable internet search libraries. This study will aid academics in analysing recent rainfall prediction work with a focus on data mining approaches, as well as providing a baseline for future directions and comparisons. Keywords: Rainfall prediction, Rainfall, Machine learning techniques.
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Taiwo Amoo, Oseni, and Bloodless Dzwairo. "Trend analysis and artificial neural networks forecasting for rainfall prediction." Environmental Economics 7, no. 4 (December 21, 2016): 149–60. http://dx.doi.org/10.21511/ee.07(4-1).2016.07.
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The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once this was accomplished, the ANNs’ accuracy was compared against a traditional forecasting method called multiple linear regression. The probability of an accurate forecast was calculated using conditional probabilities for the two models. Given the accuracy of the forecast, the benefits of the ANNs as a vital tool for decision makers in mitigating drought related concerns was enunciated. Keywords: artificial neural networks, drought, rainfall case forecast, multiple linear regression. JEL Classification: C53, C45
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Mohd, Razeef, Muheet Ahmed Butt, and Majid Zaman Baba. "Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)." Asian Journal of Computer Science and Technology 7, no. 3 (November 5, 2018): 13–19. http://dx.doi.org/10.51983/ajcst-2018.7.3.1901.
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Prediction of rainfall is one of the most essential and demanding tasks for the weather forecasters since ages. Rainfall prediction plays an important role in the field of farming and industries. Precise rainfall prediction is vital for detecting the heavy rainfall and to provide the information of warnings regarding the natural calamities. Rainfall prediction involves recording the various parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. From last few decades, it has been seen that data mining techniques have achieved good performance and accuracy in weather prediction than traditional statistical methods. This research work aims to compare the performance of few data mining algorithms for predicting rainfall using historical weather data of Srinagar, India, which is collected from http://www.wundergrounds.com website. From the collected weather data which comprises of 9 attributes, only 5 attributes which are most relevant to rainfall prediction are considered. Data mining process model is followed to obtain accurate and correct prediction results. In this paper, various data mining algorithms were explored which include decision tree based J48, Random forest, Naive Bayes, Bayes Net, Logistic Regression, IBk, PART and bagging. The experimental results show that J48 algorithm has good level of accuracy than other algorithms.
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Manandhar, Shilpa, Soumyabrata Dev, Yee Hui Lee, Yu Song Meng, and Stefan Winkler. "A Data-Driven Approach for Accurate Rainfall Prediction." IEEE Transactions on Geoscience and Remote Sensing 57, no. 11 (November 2019): 9323–31. http://dx.doi.org/10.1109/tgrs.2019.2926110.
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Subhan Panji Cipta. "PENERAPAN ALGORITMA EVOLVING NEURAL NETWORK UNTUK PREDIKSI CURAH HUJAN." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 1, no. 1 (January 13, 2016): 1–8. http://dx.doi.org/10.20527/jtiulm.v1i1.2.
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Weather and climate information have contributed as one consideration for decision makers. This arises because the information the weather / climate has economic value in a variety of activities , ranging from agriculture to flood control . From the data obtained implied that the current rainfall prediction not so accurate . Forecasts are often given to the public on a regular basis is the weather forecast , not the amount of rainfall. This study uses an algorithm Evolving Neural Network (ENN) as an approach to predict the rainfall , the data processing and calculations will use MatLab 2009b . The parameters used in this study is time , rainfall , humidity and temperature. The results also compared with the test results and predictions BPNN BMKG. From the results of research conducted from early stage to test and measurement , the application of this ENN has a rainfall prediction with accuracy better than the BPNN and prediction algorithms BMKG.
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Et.al, S. Sakthivel. "Effective Procedure to Predict Rainfall Conditions using Hybrid Machine Learning Strategies." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 209–16. http://dx.doi.org/10.17762/turcomat.v12i6.1291.
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In the present information technology stream supports many natural disaster prediction schemes to save several people from disaster scenarios. In such case, rainfall prediction and analysis is the most important concern to take care as well as the prediction of high rainfall saves many individual's life and their assets. This kind of rainfall prediction schemes provides a facilitation to take respective precautions to avoid huge damages further. The rainfall predictions are categorized into two different variants such as Limited Period Rainfall Prediction and the long period Continuous Rainfall Prediction. Several past analysis and literatures provide accurate predictions for limited period rainfall but the major problem is to identify or predict the continuous long period rainfall. This kind of drawbacks leads many researchers to work on this domain and predict the rainfall status exactly for both limited period as well as long period continues rainfall. In this paper, a new hybrid machine learning strategy is implemented to predict the rainfall status exactly, in which the proposed methodology is named as Intense Neural Network Mining (INNM). This proposed approach of INNM analyze the rainfall prediction scenario based on two different machine learning logics such as Back Propagation Neural Network and the Rapid Miner. The general machine learning algorithms train the machine with respect to the dataset features and predict the result based on testing input. In this approach two different variants of machine learning principles are utilized to classify the resulting nature with better accuracy levels and cross-validations are providing best probabilistic results in outcome. And these two logics are integrated together to produce a new hybrid machine learning strategy to predict the rainfall status exactly and save human life against disasters. In this paper, a novel dataset is utilized from Regional Meteorological Centre Chennai to predict the rainfall summary in clear manner and the summarization of specific dataset is described on further sections. The proposed approach of INNM assures the resulting accuracy levels around 96.5% in prediction with lowest error ratio of 0.04% and the resulting portion of this paper provides a proper proof of this outcome in graphical manner.
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Skarlatos, Kyriakos, Eleni S. Bekri, Dimitrios Georgakellos, Polychronis Economou, and Sotirios Bersimis. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques." Energies 16, no. 3 (February 2, 2023): 1459. http://dx.doi.org/10.3390/en16031459.
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Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.
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Ardi, Yuan, Syahril Effendi, and Erna Budhiarti Nababan. "Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction." Randwick International of Social Science Journal 2, no. 2 (April 30, 2021): 176–92. http://dx.doi.org/10.47175/rissj.v2i2.240.
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Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules
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Billah, Mustain, Md Nasim Adnan, Mostafijur Rahman Akhond, Romana Rahman Ema, Md Alam Hossain, and Syed Md. Galib. "Rainfall prediction system for Bangladesh using long short-term memory." Open Computer Science 12, no. 1 (January 1, 2022): 323–31. http://dx.doi.org/10.1515/comp-2022-0254.
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Abstract Rainfall prediction is a challenging task and has extreme significance in weather forecasting. Accurate rainfall prediction can play a great role in agricultural, aviation, natural phenomenon, flood, construction, transport, etc. Weather or climate is assumed to be one of the most complex systems. Again, chaos, also called as “butterfly effect,” limits our ability to make weather predictable. So, it is not easy to predict rainfall by conventional machine learning approaches. However, several kinds of research have been proposed to predict rainfall by using different computational methods. To accomplish chaotic rainfall prediction system for Bangladesh, in this study, historical data set-driven long short term memory (LSTM) networks method has been used, which overcomes the complexities and chaos-related problems faced by other approaches. The proposed method has three principal phases: (i) The most useful 10 features are chosen from 20 data attributes. (ii) After that, a two-layer LSTM model is designed. (iii) Both conventional machine learning approaches and recent works are compared with the LSTM model. This approach has gained 97.14% accuracy in predicting rainfall (in millimeters), which outperforms the state-of-the-art solutions. Also, this work is a pioneer work to the rainfall prediction system for Bangladesh.
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Wang, Wenchuan, Yujin Du, Kwokwing Chau, Haitao Chen, Changjun Liu, and Qiang Ma. "A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition." Water 13, no. 20 (October 14, 2021): 2871. http://dx.doi.org/10.3390/w13202871.
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Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid- and long-term rainfall forecasting.
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Kim, Jong-Suk, Anxiang Chen, Junghwan Lee, Il-Ju Moon, and Young-Il Moon. "Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific." Remote Sensing 12, no. 24 (December 17, 2020): 4133. http://dx.doi.org/10.3390/rs12244133.
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Typhoons or mature tropical cyclones (TCs) can affect inland areas of up to hundreds of kilometers with heavy rains and strong winds, along with landslides causing numerous casualties and property damage due to concentrated precipitation over short time periods. To reduce these damages, it is necessary to accurately predict the rainfall induced by TCs in the western North Pacific Region. However, despite dramatic advances in observation and numerical modeling, the accuracy of prediction of typhoon-induced rainfall and spatial distribution remains limited. The present study offers a statistical approach to predicting the accumulated rainfall associated with typhoons based on a historical storm track and intensity data along with observed rainfall data for 55 typhoons affecting the southeastern coastal areas of China from 1961 to 2017. This approach is shown to provide an average root mean square error of 51.2 mm across 75 meteorological stations in the southeast coastal area of China (ranging from 15.8 to 87.3 mm). Moreover, the error is less than 70 mm for most stations, and significantly lower in the three verification cases, thus demonstrating the feasibility of this approach. Furthermore, the use of fuzzy C-means clustering, ensemble averaging, and corrections to typhoon intensities, can provide more accurate rainfall predictions from the method applied herein, thus allowing for improvements to disaster preparedness and emergency response.
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Rivolta, G., F. S. Marzano, E. Coppola, and M. Verdecchia. "Artificial neural-network technique for precipitation nowcasting from satellite imagery." Advances in Geosciences 7 (February 2, 2006): 97–103. http://dx.doi.org/10.5194/adgeo-7-97-2006.
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Abstract. The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated.
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Wei, Chih-Chiang, and Chen-Chia Hsu. "Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan." Sensors 21, no. 4 (February 18, 2021): 1421. http://dx.doi.org/10.3390/s21041421.
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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.
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Kato, Ryohei, Ken-ichi Shimose, and Shingo Shimizu. "Predictability of Precipitation Caused by Linear Precipitation Systems During the July 2017 Northern Kyushu Heavy Rainfall Event Using a Cloud-Resolving Numerical Weather Prediction Model." Journal of Disaster Research 13, no. 5 (October 1, 2018): 846–59. http://dx.doi.org/10.20965/jdr.2018.p0846.
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Torrential rainfall associated with linear precipitation systems occurred in Northern Kyushu, Japan, during July 5–6, 2017, causing severe damage in Fukuoka and Oita Prefectures. According to our statistical survey using ground rain gauges, the torrential rainfall was among the heaviest in recorded history for 6- and 12-h accumulated rainfall, and was unusual because heavy rain continued locally for nine hours. The predictability of precipitation associated with linear precipitation systems for this event was investigated using a cloud-resolving numerical weather prediction model with a horizontal grid interval of 1 km. The development of multiple linear precipitation systems was predicted in experiments whose initial calculation time was from several hours to immediately before the torrential rain (9:00, 10:00, 11:00, and 12:00 Japan Standard Time on July 5), although there were some displacement errors in the predicted linear precipitation systems. However, the stationary linear precipitation systems were not properly predicted. The predictions showed that the linear precipitation systems formed one after another and moved eastwards. In the relatively accurate prediction whose initial time was 12:00 on July 5, immediately before the torrential rainfall began, the forecast accuracy was evaluated using the 6-h accumulated precipitation (P6h) from 12:00 to 18:00 on July 5, the period of the heaviest rainfall. The average of the P6h in an area 100 km×40 km around the torrential rainfall area was nearly the same for the analysis and the prediction, indicating that the total precipitation amount around the torrential rainfall area was predictable. The result of evaluating the quantitative prediction accuracy using the Fractions Skill Score (FSS) indicated that a difference in location of 25 km (50 km) or greater should be allowed for in the models to produce useful predictions (those defined as having an FSS ≥0.5) for the accumulated rainfall of P6h ≥50 mm (150 mm). The quantitative prediction accuracy examined in this study can be basic information to investigate the usage of predicted precipitation data.
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Hearman, A. J., and C. Hinz. "Sensitivity of point scale surface runoff predictions to rainfall resolution." Hydrology and Earth System Sciences 11, no. 2 (March 5, 2007): 965–82. http://dx.doi.org/10.5194/hess-11-965-2007.
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Abstract. This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2) for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.
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FAYAZ, Sheikh Amir, Majid ZAMAN, Muheet Ahmed BUTT, and Sameer KAUL. "HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS." Applied Computer Science 18, no. 4 (October 1, 2022): 16–27. http://dx.doi.org/10.35784/acs-2022-26.
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Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.
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Desai, V. P., R. K. Kamat, and K. S. Oza. "Rainfall Modeling and Prediction using Neural Networks: A Case Study of Maharashtra." Disaster Advances 15, no. 3 (February 25, 2022): 39–43. http://dx.doi.org/10.25303/1503da3943.
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In different parts of the globe, accurate rainfall forecasting models are needed to forecast rainfall in real-time during the typhoon season to prevent disasters caused by heavy rainfall in the region. The current study developed a time series based analysis technique to predict precipitation during the monsoon season. As evidenced by the investigations in this study, a data-driven technique has considerable scope for predicting the future variables and patterns from existing data, mainly when applied to complex and challenging natural phenomena such as rainfall. The ANN-based technique has an immense potential to predict rainfall data using lagged time series analysis model. We have investigated rainfall patterns, their variability in Maharashtra state and future rainfall prediction through the present study. The neural network autoregression model put forth is a promising technique for rainfall prediction. The model's performance is evaluated concerning error rate and model fit and exhibits reasonably good performance.
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Nakashima, Mitsuhiro, Shoichi Sameshima, Yuki Kimura, and Midori Yoshimoto. "Evaluation of Real-Time Water Level Prediction Technology Using Statistical Models for Reducing Urban Flood Risk." Journal of Disaster Research 16, no. 3 (April 1, 2021): 387–94. http://dx.doi.org/10.20965/jdr.2021.p0387.
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The frequency of localized short-term torrential rains that exceed the planned rainfall is increasing along with inundation damage due to inland flooding. Stepwise inundation measures utilizing existing stock and disaster prevention/mitigation for excessive rainfall are required. In this study, we describe the results of empirical research using a statistical model constructed based on rainfall and water level observation data as a highly accurate water level prediction method suitable for real-time prediction. This is aimed at application in flood control activities and operation support of pump facilities. By comparing and verifying the prediction accuracy between the water level prediction model and the statistical model by Convolutional Neural Network (CNN), which is generally used as an image recognition technology, the usefulness of the statistical model was confirmed. Further improvement in accuracy and widespread use of these water level prediction models are expected.
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Salaeh, Nureehan, Pakorn Ditthakit, Sirimon Pinthong, Mohd Abul Hasan, Saiful Islam, Babak Mohammadi, and Nguyen Thi Thuy Linh. "Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand." Symmetry 14, no. 8 (August 3, 2022): 1599. http://dx.doi.org/10.3390/sym14081599.
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Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead.
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Khan, Roohul Abad, Rachida El Morabet, Javed Mallick, Mohammed Azam, Viola Vambol, Sergij Vambol, and Volodymyr Sydorenko. "Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia." Ecological Questions 32, no. 4 (September 24, 2021): 1–11. http://dx.doi.org/10.12775/eq.2021.038.
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Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.
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Rahman, Atta-ur, Sagheer Abbas, Mohammed Gollapalli, Rashad Ahmed, Shabib Aftab, Munir Ahmad, Muhammad Adnan Khan, and Amir Mosavi. "Rainfall Prediction System Using Machine Learning Fusion for Smart Cities." Sensors 22, no. 9 (May 4, 2022): 3504. http://dx.doi.org/10.3390/s22093504.
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Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.
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Han, Heechan, Changhyun Choi, Jaewon Jung, and Hung Soo Kim. "Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation." Water 13, no. 4 (February 8, 2021): 437. http://dx.doi.org/10.3390/w13040437.
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Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results.
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Lee, Sanghyup, Yeonjeong Seong, and Younghun Jung. "LENS-GRM Applicability Analysis and Evaluation." Water 14, no. 23 (November 30, 2022): 3897. http://dx.doi.org/10.3390/w14233897.
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Recently, there have been many abnormal natural phenomena caused by climate change. Anthropogenic factors associated with insufficient water resource management can be another cause. Among natural causes, rainfall intensity and volume often induce flooding. Therefore, accurate rainfall estimation and prediction can prevent and mitigate damage caused by these hazards. Sadly, uncertainties often hinder accurate rainfall forecasting. This study investigates the uncertainty of the Korean rainfall ensemble prediction data and runoff analysis model in order to enhance reliability and improve prediction. The objectives of this study include: (i) evaluating the spatial characteristics and applicability of limited area ensemble prediction system (LENS) data; (ii) understanding uncertainty using parameter correction and generalized likelihood uncertainty estimation (GLUE) and grid-based rainfall-runoff model (GRM); (iii) evaluating models before and after LENS-GRM correction. In this study, data from the Wicheon Basin was used. The informal likelihood (R2, NSE, PBIAS) and formal likelihood (log-normal) were used to evaluate model applicability. The results confirmed that uncertainty of the behavioral model exists using the likelihood threshold when applying the runoff model to rainfall forecasting data. Accordingly, this method is expected to enable more reliable flood prediction by reducing the uncertainties of the rainfall ensemble data and the runoff model when selecting the behavioral model for the user’s uncertainty analysis. It also provides a basis for flood prediction studies that apply rainfall and geographical characteristics for rainfall-runoff uncertainty analysis.
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Wijesundera, Isuri, Malka N. Halgamuge, Thas Nirmalathas, and Thrishantha Nanayakkara. "A Geographic Primitive-Based Bayesian Framework to Predict Cyclone-Induced Flooding*." Journal of Hydrometeorology 14, no. 2 (April 1, 2013): 505–23. http://dx.doi.org/10.1175/jhm-d-12-040.1.
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Abstract The effectiveness of managing cyclone-induced floods is highly dependent on how fast reasonably accurate predictions can be made, which is a particularly difficult task given the multitude of highly variable physical factors. Even with supercomputers, collecting and processing vast amounts of data from numerous asynchronous sources makes it challenging to achieve high prediction efficiency. This paper presents a model that combines prior knowledge, including rainfall data statistics and topographical features, with any new precipitation data to generate a probabilistic prediction using Bayesian learning, where the advantages of data-oriented and heuristic modeling are combined. The terrain is partitioned into geographic primitives (GPs) based on manual inspection of flood propagation vector fields in order to simplify the stochastic system identification. High calculation efficiency is achieved through statistically summarizing simultaneous events spread across geography into primitives, allowing a distributed updating algorithm leading to parallel computing. Markov chain processes identified for each of these GPs, based on both simulation and measured rainfall data, are then used in real-time predictions of water flow probabilities. The model takes a comprehensive approach, which enables flood prediction even before the landfall of a cyclone through modularizing the algorithm into three prediction steps: cyclone path, rainfall probability density distribution, and temporal dynamics of flood density distribution. Results of comparative studies based on real data of two cyclones (Yasi and Tasha) that made landfall in Queensland, Australia, in 2010/11 show that the model is capable of predicting up to 3 h ahead of the official forecast, with a 33% improvement of accuracy compared to the models presently being used.
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Chao, Zeyi, Fangling Pu, Yuke Yin, Bin Han, and Xiaoling Chen. "Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors." Journal of Sensors 2018 (June 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/6184713.
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A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.
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Dabhi, Vipul K., and Sanjay Chaudhary. "Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India." Advances in Artificial Intelligence 2014 (June 2, 2014): 1–11. http://dx.doi.org/10.1155/2014/717803.
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An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise) component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.
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Kumar, Pushpendra, A. K. Lohani, and A. K. Nema. "Rainfall Runoff Modeling Using MIKE 11 Nam Model." Current World Environment 14, no. 1 (April 25, 2019): 27–36. http://dx.doi.org/10.12944/cwe.14.1.05.
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River basin planning and management are primarily based on the accurate assessment and prediction of catchment runoff. A continuous effort has been made by the various researchers to accurately assess the runoff generated from precipitation by developing various models. In this paper conceptual hydrological MIKE 11 NAM approach has been used for developing a runoff simulation model for Arpasub-basin of Seonath river basin in Chhattisgarh, India. NAM model has been calibrated and validated using discharge data at Kota gauging site on Arpa basin. The calibration and validation results show that this model is capable to define the rainfall runoff process of the basin and thus predicting daily runoff. The ability of the NAM model in rainfall runoff modelling of Arpa basin was assessed using Nash–Sutcliffe Efficiency Index (EI), coefficient of determination (R2) and Root Mean Square Error (RMSE). This study demonstrates the usefulness of the developed model for the runoff prediction in the Arpa basin which acts as a useful input for the integrated water resources development and management at the basin scale.
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Gauch, Martin, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter. "Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network." Hydrology and Earth System Sciences 25, no. 4 (April 19, 2021): 2045–62. http://dx.doi.org/10.5194/hess-25-2045-2021.
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Abstract. Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a lifesaving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning difficult and computationally expensive. In this study, we propose two multi-timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a different temporal resolution than more recent inputs. In a benchmark on 516 basins across the continental United States, these models achieved significantly higher Nash–Sutcliffe efficiency (NSE) values than the US National Water Model. Compared to naive prediction with distinct LSTMs per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.
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Jia, Yuhan, Jianping Wu, and Ming Xu. "Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method." Journal of Advanced Transportation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6575947.
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Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.
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Sun, Dechao, Jiali Wu, Hong Huang, Renfang Wang, Feng Liang, and Hong Xinhua. "Prediction of Short-Time Rainfall Based on Deep Learning." Mathematical Problems in Engineering 2021 (March 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/6664413.
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Short-time heavy rainfall is a kind of sudden strong and heavy precipitation weather, which seriously threatens people’s life and property safety. Accurate precipitation nowcasting is of great significance for the government to make disaster prevention and mitigation decisions in time. In order to make high-resolution forecasts of regional rainfall, this paper proposes a convolutional 3D GRU (Conv3D-GRU) model to predict the future rainfall intensity over a relatively short period of time from the machine learning perspective. Firstly, the spatial features of radar echo maps with different heights are extracted by 3D convolution, and then, the radar echo maps on time series are coded and decoded by using GRU. Finally, the trained model is used to predict the radar echo maps in the next 1-2 hours. The experimental results show that the algorithm can effectively extract the temporal and spatial features of radar echo maps, reduce the error between the predicted value and the real value of rainfall, and improve the accuracy of short-term rainfall prediction.
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Wei, Chih-Chiang, and Tzu-Hao Chou. "Typhoon Quantitative Rainfall Prediction from Big Data Analytics by Using the Apache Hadoop Spark Parallel Computing Framework." Atmosphere 11, no. 8 (August 17, 2020): 870. http://dx.doi.org/10.3390/atmos11080870.
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Situated in the main tracks of typhoons in the Northwestern Pacific Ocean, Taiwan frequently encounters disasters from heavy rainfall during typhoons. Accurate and timely typhoon rainfall prediction is an imperative topic that must be addressed. The purpose of this study was to develop a Hadoop Spark distribute framework based on big-data technology, to accelerate the computation of typhoon rainfall prediction models. This study used deep neural networks (DNNs) and multiple linear regressions (MLRs) in machine learning, to establish rainfall prediction models and evaluate rainfall prediction accuracy. The Hadoop Spark distributed cluster-computing framework was the big-data technology used. The Hadoop Spark framework consisted of the Hadoop Distributed File System, MapReduce framework, and Spark, which was used as a new-generation technology to improve the efficiency of the distributed computing. The research area was Northern Taiwan, which contains four surface observation stations as the experimental sites. This study collected 271 typhoon events (from 1961 to 2017). The following results were obtained: (1) in machine-learning computation, prediction errors increased with prediction duration in the DNN and MLR models; and (2) the system of Hadoop Spark framework was faster than the standalone systems (single I7 central processing unit (CPU) and single E3 CPU). When complex computation is required in a model (e.g., DNN model parameter calibration), the big-data-based Hadoop Spark framework can be used to establish highly efficient computation environments. In summary, this study successfully used the big-data Hadoop Spark framework with machine learning, to develop rainfall prediction models with effectively improved computing efficiency. Therefore, the proposed system can solve problems regarding real-time typhoon rainfall prediction with high timeliness and accuracy.
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Capecchi, V., M. Perna, and A. Crisci. "Statistical modeling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results." Natural Hazards and Earth System Sciences Discussions 2, no. 8 (August 4, 2014): 4987–5036. http://dx.doi.org/10.5194/nhessd-2-4987-2014.
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Abstract. We present a quantitative indirect statistical modeling for predicting rainfall-induced shallow landsliding. We consider as input layers both static thematic predictors, such as geomorphological, geological, climatological information, and numerical weather model's forecast. Two different statistical techniques are used to combine together the above mentioned predictors: a Generalized Linear Model and Breiman's Random Forests. We tested these two techniques for two rainfall events that occurred in 2011 and 2013 in Tuscany region (central Italy). Model's evaluation is measured by means of sensitivity-specificity ROC analysis. In the 2011 rainfall event, the Random Forests technique performs slightly better, whereas in the 2013 rainfall event the Generalized Linear Model provides more accurate predictions. This study seeks also to establish whether the rainfall-induced shallow landsliding prediction might substantially benefit from the information provided by the numerical weather model's outputs. Using the variable importance parameter provided by the Random Forests algorithm, we asses the added value carried by numerical weather forecast, in particular in the rainfall event characterized by deep atmospheric convection and heavy precipitations.
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Klotz, Daniel, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing. "Uncertainty estimation with deep learning for rainfall–runoff modeling." Hydrology and Earth System Sciences 26, no. 6 (March 31, 2022): 1673–93. http://dx.doi.org/10.5194/hess-26-1673-2022.
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Abstract. Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological prediction, and while standardized community benchmarks are becoming an increasingly important part of hydrological model development and research, similar tools for benchmarking uncertainty estimation are lacking. This contribution demonstrates that accurate uncertainty predictions can be obtained with deep learning. We establish an uncertainty estimation benchmarking procedure and present four deep learning baselines. Three baselines are based on mixture density networks, and one is based on Monte Carlo dropout. The results indicate that these approaches constitute strong baselines, especially the former ones. Additionally, we provide a post hoc model analysis to put forward some qualitative understanding of the resulting models. The analysis extends the notion of performance and shows that the model learns nuanced behaviors to account for different situations.
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Unnikrishnan, Poornima, and V. Jothiprakash. "Data-driven multi-time-step ahead daily rainfall forecasting using singular spectrum analysis-based data pre-processing." Journal of Hydroinformatics 20, no. 3 (August 9, 2017): 645–67. http://dx.doi.org/10.2166/hydro.2017.029.
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Abstract Accurate forecasting of rainfall, especially daily time-step rainfall, remains a challenging task for hydrologists' invariance with the existence of several deterministic, stochastic and data-driven models. Several researchers have fine-tuned the hydrological models by using pre-processed input data but improvement rate in prediction of daily time-step rainfall data is not up to the expected level. There are still chances to improve the accuracy of rainfall predictions with an efficient data pre-processing algorithm. Singular spectrum analysis (SSA) is one such technique found to be a very successful data pre-processing algorithm. In the past, the artificial neural network (ANN) model emerged as one of the most successful data-driven techniques in hydrology because of its ability to capture non-linearity and a wide variety of algorithms. This study aims at assessing the advantage of using SSA as a pre-processing algorithm in ANN models. It also compares the performance of a simple ANN model with SSA-ANN model in forecasting single time-step as well as multi-time-step (3-day and 7-day) ahead daily rainfall time series pertaining to Koyna watershed, India. The model performance measures show that data pre-processing using SSA has enhanced the performance of ANN models both in single as well as multi-time-step ahead daily rainfall prediction.
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Farhan, Ahmad, Yopi Ilhamsyah, and Akhyar. "The Use of SST Anomaly to Predict Seasonal Rainfall during the Second Planting Period in the Tanoh Abee Irrigation Area, Aceh Besar." Agromet 34, no. 2 (September 30, 2020): 100–109. http://dx.doi.org/10.29244/j.agromet.34.2.100-109.
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Irrigation in Tanoh Abee is used for agricultural activities especially during the second planting season during dry season. However, the availability of irrigation water is controlled by total rainfall received. An accurate prediction of rainfall, which traditionally used “keneunong” local wisdom, is urgently required. The objective of the study is to obtain the best predictor of seasonal rainfall based on the Pacific sea surface temperature (SST) anomaly and the monthly lead time of prediction. We employed monthly rainfall from six stations surrounding the study area and combined with principal component analysis to eliminate rainfall autocorrelation. Seasonal rainfall (quarterly average) was calculated from monthly data. The results showed that 1-month lead time strongly correlated to seasonal rainfall in Tanoh Abee (r<-0.7, α=5%) for the second planting period. On other hand, the 2-month and 3-month lead time were useful to predict seasonal rainfall in March-April-May (MAM) only. For April-May-June (AMJ) and May-June-July (MJJ), the correlation between SST anomaly and seasonal rainfall was weak. This finding indicated that the accuracy of prediction decreases with the longer lead time. Based on our analysis, coordinates of 170° E – 175° E; 5° N - 5° S in Niño 4 region have strongly correlated with seasonal rainfall in MAM, AMJ, and MJJ periods. Moreover, further research is necessary to combine any approaches that will improve our prediction skill for another 2- or 3-month lead time.
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Yang, Young-Min, Bin Wang, and Juan Li. "Improving Seasonal Prediction of East Asian Summer Rainfall Using NESM3.0: Preliminary Results." Atmosphere 9, no. 12 (December 8, 2018): 487. http://dx.doi.org/10.3390/atmos9120487.
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It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.
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Li, Bohao, Kai Liu, Ming Wang, Qian He, Ziyu Jiang, Weihua Zhu, and Ningning Qiao. "Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning." Remote Sensing 14, no. 22 (November 16, 2022): 5795. http://dx.doi.org/10.3390/rs14225795.
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Precipitation is the main factor that triggers landslides. Rainfall-induced landslide susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation, though most studies are temporally ambiguous and on a regional scale. To better reveal landslide mechanisms and provide more accurate landslide susceptibility maps for landslide risk assessment and hazard prediction, developing a global dynamic LSM model is essential. In this study, we used Google Earth Engine (GEE) as the main data platform and applied three tree-based ensemble machine learning algorithms to construct global, dynamic rainfall-induced LSM models based on dynamic and static landslide influencing factors. The dynamic perspective is used in LSM: dynamic changes in landslide susceptibility can be identified on a daily scale. We note that Random Forest algorithm offers robust performance for accurate LSM (AUC = 0.975) and although the classification accuracy of LightGBM is the highest (AUC = 0.977), the results do not meet the sufficient conditions of a landslide susceptibility map. Combined with quantitative precipitation products, the proposed model can be used for the release of historical and predictive global dynamic landslide susceptibility information.
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Wei, Chih-Chiang. "Improvement of Typhoon Precipitation Forecast Efficiency by Coupling SSM/I Microwave Data with Climatologic Characteristics and Precipitation." Weather and Forecasting 28, no. 3 (June 1, 2013): 614–30. http://dx.doi.org/10.1175/waf-d-12-00089.1.
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Abstract Prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. This study aims to address the rainfall prediction problem for quantitative precipitation forecasts over land during typhoons. To improve the efficiency of forecasting typhoon precipitation, this study develops Bayesian network (BN) and logistic regression (LR) models using three different datasets and examines their feasibility under different rain intensities. The study area is the watershed of the Tanshui River in Taiwan. The dataset includes a total of 70 typhoon events affecting the watershed from 1997 to 2008. For practicability, the three datasets used include climatologic characteristics of typhoons issued by the Central Weather Bureau (CWB), rainfall rates measured using automatic meteorological gauges in the watershed, and microwave data originated from Special Sensor Microwave Imager (SSM/I) radiometers. Five separate BN and LR models (cases), differentiated by a unique combination of input datasets, were tested, and their predicted rainfalls are compared in terms of skill scores including mean absolute error (MAE), RMSE, bias (BIA), equitable threat score (ETS), and precision (PRE). The results show that the case where all three input datasets are used is better than the other four cases. Moreover, LR can provide better predictions than BN, especially in flash rainfall situations. However, BN might be one of the most prominent approaches when considering the ease of knowledge interpretation. In contrast, LR describes associations, not causes, and does not explain the decision.
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Ekanayake, Piyal, Lasini Wickramasinghe, J. M. Jeevani W. Jayasinghe, and Upaka Rathnayake. "Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning." Mathematical Problems in Engineering 2021 (July 31, 2021): 1–12. http://dx.doi.org/10.1155/2021/4913824.
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This paper presents the development of models for the prediction of power generation at the Samanalawewa hydropower plant, which is one of the major power stations in Sri Lanka. Four regression-based machine learning and statistical techniques were applied to develop the prediction models. Rainfall data at six locations in the catchment area of the Samanalawewa reservoir from 1993 to 2019 were used as the main input variables. The minimum and maximum temperature and evaporation at the reservoir site were also incorporated. The collinearities between the variables were investigated in terms of Pearson’s and Spearman’s correlation coefficients. It was found that rainfall at one location is less impactful on power generation, while that at other locations are highly correlated with each other. Prediction models based on monthly and quarterly data were developed, and their performance was evaluated in terms of the correlation coefficient (R), mean absolute percentage error (MAPE), ratio of the root mean square error (RMSE) to the standard deviation of measured data (RSR), BIAS, and the Nash number. Of the Gaussian process regression (GPR), support vector regression (SVR), multiple linear regression (MLR), and power regression (PR), the machine learning techniques (GPR and SVR) produced the comparably accurate prediction models. Being the most accurate prediction model, the GPR produced the best correlation coefficient closer to 1 with a very less error. This model could be used in predicting the hydropower generation at the Samanalawewa power station using the rainfall forecast.
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Wang, Shaobo, Wanhua Yuan, and Jiawei Zhou. "Analysis of Runoff Coefficient Prediction Based on LM-BP Neural Network." Journal of Physics: Conference Series 2333, no. 1 (August 1, 2022): 012020. http://dx.doi.org/10.1088/1742-6596/2333/1/012020.
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Abstract Runoff coefficients are affected by many factors, and their complex nonlinear relationships make it difficult to calculate accurate runoff coefficients using experimental physical models. In this paper, we improved the traditional BP neural network model based on the Levenberg-Marquardt method and established an S-type/S-type mathematical model of the relationship between runoff coefficients and influencing factors to predict each surface runoff coefficient under different rainfall conditions and different subsurface conditions, and compared it with other methods. The results calculated after the actual case simulation showed that the error of LM-BP neural network simulation was within the range of 0.03~0.09, the error was smaller, the calculation results were more accurate, and the prediction of runoff coefficient had the advantages of strong generalization ability and high prediction accuracy, which was a great improvement to the traditional rainfall-runoff coefficient best-fit relational fitting relationship method. Besides, in order to reduce the problem of sample overtraining, the more detailed the hydrological information, the better.
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Kumar, Ashish, Anshika Varshney, Ankita Arya, and Manjot Kaur Bhatia. "Survey on Rainfall Prediction using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 231–35. http://dx.doi.org/10.22214/ijraset.2022.47842.
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Abstract: One of the most significant and difficult tasks in the modern world is rainfall forecasting. Generally speaking, climate and rainfall are extremely complex and non-linear phenomena those that demand sophisticated computer modelling and simulation for making an accurate forecast. Asynthetic neural network (ANN) can be used to forecast how such nonlinear systems will behave. Most of the industries have adopted ANN with success in this subject for the past 25 years as researchers. This paper offers a review of the literature on some study methods used by many scholars to use ANN for predicting rainfall. Additionally, the study notes that The ANN approach is more suitable for predicting rain than standard numerical and statistical techniques.
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Ashra M. Ashri, Aisar, Wardah Tahir, Nurul Syahira M. Harmay, Intan Shafeenar A. Mohtar, Sazali Osman, and Wan Hazdy Azad Wan Abdul Majid. "Statistical Verification of Numerical Weather Prediction (NWP) for Rainfall Estimation in the East Coast Region." International Journal of Engineering & Technology 7, no. 3.11 (July 21, 2018): 168. http://dx.doi.org/10.14419/ijet.v7i3.11.15954.
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Intense hydrological event such as floods are increasing lately especially in Peninsular Malaysia. Therefore, it is important to forecast the intense rainfall as part of flood preparedness and mitigation measures. In this study, Numerical Weather Prediction (NWP) model precipitation outputs using Weather Research and Forecasting (WRF) with horizontal resolution of 3 km have been validated against observed rainfall data measurements for its performance measurement. Forecasted rainfall event data of three (3) states in the East Coast Region; Kelantan, Terengganu and Pahang were evaluated and compared with the observed rainfall data before statistically verifying their accuracy using False Alarm Ratio (FAR) and Probability of Detection (POD). The results indicate a very promising potential of the models in producing quantitative precipitation forecast (QPF) for flood forecasting purpose in Kelantan, Terengganu and Pahang. Since these three states, which are located in the East Coast region of Peninsular Malaysia experienced annual flood event, accurate forecast rainfall data can be used to improve forecast information for flood indicator.
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Slocum, Matthew G., William J. Platt, Brian Beckage, Steve L. Orzell, and Wayne Taylor. "Accurate Quantification of Seasonal Rainfall and Associated Climate–Wildfire Relationships." Journal of Applied Meteorology and Climatology 49, no. 12 (December 1, 2010): 2559–73. http://dx.doi.org/10.1175/2010jamc2532.1.
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Abstract Wildfires are often governed by rapid changes in seasonal rainfall. Therefore, measuring seasonal rainfall on a temporally finescale should facilitate the prediction of wildfire regimes. To explore this hypothesis, daily rainfall data over a 58-yr period (1950–2007) in south-central Florida were transformed into cumulative rainfall anomalies (CRAs). This transformation allowed precise estimation of onset dates and durations of the dry and wet seasons, as well as a number of other variables characterizing seasonal rainfall. These variables were compared with parameters that describe ENSO and a wildfire regime in the region (at the Avon Park Air Force Range). Onset dates and durations were found to be highly variable among years, with standard deviations ranging from 27 to 41 days. Rainfall during the two seasons was distinctive, with the dry season having half as much as the wet season despite being nearly 2 times as long. The precise quantification of seasonal rainfall led to strong statistical models describing linkages between climate and wildfires: a multiple-regression technique relating the area burned with the seasonal rainfall characteristics had an of 0.61, and a similar analysis examining the number of wildfires had an of 0.56. Moreover, the CRA approach was effective in outlining how seasonal rainfall was associated with ENSO, particularly during the strongest and most unusual events (e.g., El Niño of 1997/98). Overall, the results presented here show that using CRAs helped to define the linkages among seasonality, ENSO, and wildfires in south-central Florida, and they suggest that this approach can be used in other fire-prone ecosystems.
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Peña, Mario, Angel Vázquez-Patiño, Darío Zhiña, Martin Montenegro, and Alex Avilés. "Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region." Advances in Meteorology 2020 (September 17, 2020): 1–17. http://dx.doi.org/10.1155/2020/1828319.
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Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere. However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods). An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans. In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations. To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified. RF and SVM also demonstrated poor performance. However, RF had a better fit than linear models, and SVM has a better fit than RF models. Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead. As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay. The predictions yielded by NARX models, for the first six months ahead, were entirely accurate. This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes. The results would serve to make short-term plans and give support to decision-makers in the management of water resources.
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Mugume, Isaac, Michel Mesquita, Yazidhi Bamutaze, Didier Ntwali, Charles Basalirwa, Daniel Waiswa, Joachim Reuder, et al. "Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda." Atmosphere 9, no. 9 (August 22, 2018): 328. http://dx.doi.org/10.3390/atmos9090328.
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Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.
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Roem, Ruminta, and Tati Nurmala. "Simulation and Prediction of Rainfall and Crop Yield in West Java Using ANFIS." Jurnal Matematika Integratif 13, no. 2 (September 26, 2017): 83. http://dx.doi.org/10.24198/jmi.v13.n2.11844.83-94.
Full textAbstract:
Simulation of numerical data for prediction purposes is very important for the planning and anticipation of the future, for example, prediction data of rainfall and agricultural production. There are various models to simulate and forecast the numerical data, one of which is a artificial intelligence model using ANFIS. In this connection it has studied a simulation and prediction of rainfall and agricultural production in West Java using ANFIS. The study uses data of rainfall and crop production. The method of this study is descriptive explanatory which is a type of quantitative analysis. Numerical data were analyzed using ANFIS of the Software Matlab 8.0. The study results showed that ANFIS can simulate rainfall and crop yield with highly accurate and has the potential to be used as one of the alternative model to predict rainfall and crop yield in West Java
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Roem, Ruminta, and Tati Nurmala. "Simulation and Prediction of Rainfall and Crop Yield in West Java Using ANFIS." Jurnal Matematika Integratif 13, no. 2 (September 26, 2017): 83. http://dx.doi.org/10.24198/jmi.v13i2.11844.
Full textAbstract:
Simulation of numerical data for prediction purposes is very important for the planning and anticipation of the future, for example, prediction data of rainfall and agricultural production. There are various models to simulate and forecast the numerical data, one of which is a artificial intelligence model using ANFIS. In this connection it has studied a simulation and prediction of rainfall and agricultural production in West Java using ANFIS. The study uses data of rainfall and crop production. The method of this study is descriptive explanatory which is a type of quantitative analysis. Numerical data were analyzed using ANFIS of the Software Matlab 8.0. The study results showed that ANFIS can simulate rainfall and crop yield with highly accurate and has the potential to be used as one of the alternative model to predict rainfall and crop yield in West Java
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ZHU, CHANGMING, and JIANSHENG WU. "HYBRID OF GENETIC ALGORITHM AND SIMULATED ANNEALING FOR SUPPORT VECTOR REGRESSION OPTIMIZATION IN RAINFALL FORECASTING." International Journal of Computational Intelligence and Applications 12, no. 02 (June 2013): 1350012. http://dx.doi.org/10.1142/s1469026813500120.
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Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.
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SP, VIJAYALAKSHMI. "Prediction of tropical cyclone induced rainfall variability over East coast of India using satellite measurements." MAUSAM 73, no. 4 (September 30, 2022): 775–84. http://dx.doi.org/10.54302/mausam.v73i4.4657.
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Rainfall intensity due to cyclonic events is very high compared to the monsoon rain, causing heavy damage to the lives of humans and cattle and another severe bruise. To minimize such damages, accurate prediction of rainfall is necessary. Adequate knowledge about the spatial distribution of precipitation and its temporal variation is essential for any analysis. The study aims at predicting the rainfall resulting from the tropical cyclone (TC) to help any relief activity or preparation of disaster mitigation plans. The detailed definition, classification, and conditions necessary for the cyclone to occur are discussed in the study to know how a hurricane originates, grows, and dissipates. GIS-based mean rainfall along the track of TC is derived from TRMM-3B42 data to develop a relationship between the cyclonic parameters and rainfall. This correlation helps to assess its impact and behaviour. A generalized regression model is developed with the sensitive parameters of TC-induced rainfall and cyclonic variables like wind speed, location, pressure, and precipitation to predict future events with predictors as the cyclonic parameters and rainfall as the response. Rainfall variability from 2008-2017 is analyzed. 2/3rd of the data (from 2008 -17) is used in analyzing part and the remaining for validation. The prediction for the GAJA cyclone resulted in a correlation value of 0.8 and 0.72 for the 16th and 17th of November 2018. The results show that the predicted value is almost the same as the actual value of rainfall that has occurred.