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Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models

artificial neural network; decision making; interpolation; numerical model; optimization; performance assessment; precipitation intensity; principal component analysis; support vector machine; water resource; Iran

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Main Authors: Banadkooki F.B., Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., Ridwam W.M., Sefelnasr A., El-Shafie A.
Other Authors: 57201068611
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Published: MDPI 2023
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spelling my.uniten.dspace-242662023-05-29T15:22:30Z Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models Banadkooki F.B. Ehteram M. Ahmed A.N. Fai C.M. Afan H.A. Ridwam W.M. Sefelnasr A. El-Shafie A. 57201068611 57113510800 57214837520 57214146115 56436626600 57218502036 6505592467 16068189400 artificial neural network; decision making; interpolation; numerical model; optimization; performance assessment; precipitation intensity; principal component analysis; support vector machine; water resource; Iran Drought, climate change, and demand make precipitation forecast a very important issuein water resource management. The present study aims to develop a forecasting model for monthlyprecipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using themultilayer perceptron neural network (MLP) and support vector regression (SVR) models. In thisstudy, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neuralnetwork and support vector machine. The flow regime optimization algorithm not only identifies theparameters of the SVR and MLP models but also replaces the training algorithms. The decision treemodel (M5T) was also used to forecast precipitation and compare it with the results of hybrid models.Principal component analysis (PCA) was used to identify effective indicators for precipitation forecast.In the first scenario, the input data include temperature data with a delay of one to twelve months,the second scenario includes precipitation data with a delay of one to twelve months, and the thirdscenario includes precipitation and temperature data with a delay of one to three months. The meanabsolute error (MAE) and Nash-Sutcliffe error (NSE) indices were used to evaluate the performanceof the models. The results showed that the proposed MLP-FRA outperformed all the other examinedmodels. Regarding the uncertainties of the models, it was also shown that the MLP-FRA modelhad a lower uncertainty band width than other models, and a higher percentage of the data will fallwithin the range of the confidence band. As the selected scenario, Scenario 3 had a better performance.Finally, monthly precipitation maps were generated based on the MLP-FRA model and Scenario3 using the weighted interpolation method, which showed significant precipitation in spring andwinter and a low level of precipitation in summer. The results of the present study showed thatMLP-FRA has high capability to predict hydrological variables and can be used in future research. � 2019 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T07:22:30Z 2023-05-29T07:22:30Z 2019 Article 10.3390/su11236681 2-s2.0-85076709919 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076709919&doi=10.3390%2fsu11236681&partnerID=40&md5=b7a8b4f792f15411c10f392dd59228da https://irepository.uniten.edu.my/handle/123456789/24266 11 23 6681 All Open Access, Gold, Green MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description artificial neural network; decision making; interpolation; numerical model; optimization; performance assessment; precipitation intensity; principal component analysis; support vector machine; water resource; Iran
author2 57201068611
author_facet 57201068611
Banadkooki F.B.
Ehteram M.
Ahmed A.N.
Fai C.M.
Afan H.A.
Ridwam W.M.
Sefelnasr A.
El-Shafie A.
format Article
author Banadkooki F.B.
Ehteram M.
Ahmed A.N.
Fai C.M.
Afan H.A.
Ridwam W.M.
Sefelnasr A.
El-Shafie A.
spellingShingle Banadkooki F.B.
Ehteram M.
Ahmed A.N.
Fai C.M.
Afan H.A.
Ridwam W.M.
Sefelnasr A.
El-Shafie A.
Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
author_sort Banadkooki F.B.
title Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
title_short Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
title_full Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
title_fullStr Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
title_full_unstemmed Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing models
title_sort precipitation forecasting using multilayer neuralnetwork and support vector machine optimization based on flow regime algorithm taking intoaccount uncertainties of soft computing models
publisher MDPI
publishDate 2023
_version_ 1806424587990204416
score 13.222552

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