Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

Fatemeh Barzegari Banadkooki, Mohammad Ehteram, Ali Najah Ahmed, Chow Ming Fai, Haitham Abdulmohsin Afan, Wani M. Ridwam, Ahmed Sefelnasr and Ahmed El-Shafie
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Fatemeh Barzegari Banadkooki: Agricultural Department, Payam Noor University, Tehran 19395-4697, Iran
Mohammad Ehteram: Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 3513119111, Iran
Ali Najah Ahmed: Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Selangor 43000, Malaysia
Chow Ming Fai: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
Haitham Abdulmohsin Afan: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Wani M. Ridwam: Department of Civil Engineering, Universiti Tenaga Nasional, (UNITEN), Selangor 43000, Malaysia
Ahmed Sefelnasr: National Water Center, United Arab Emirate University, P.O. Box Al Ain 15551, UAE
Ahmed El-Shafie: Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia

Sustainability, 2019, vol. 11, issue 23, 1-21

Abstract: Drought, climate change, and demand make precipitation forecast a very important issue in water resource management. The present study aims to develop a forecasting model for monthly precipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using the multilayer perceptron neural network (MLP) and support vector regression (SVR) models. In this study, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neural network and support vector machine. The flow regime optimization algorithm not only identifies the parameters of the SVR and MLP models but also replaces the training algorithms. The decision tree model (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 third scenario includes precipitation and temperature data with a delay of one to three months. The mean absolute error (MAE) and Nash–Sutcliffe error (NSE) indices were used to evaluate the performance of the models. The results showed that the proposed MLP–FRA outperformed all the other examined models. Regarding the uncertainties of the models, it was also shown that the MLP–FRA model had a lower uncertainty band width than other models, and a higher percentage of the data will fall within 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 Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP–FRA has high capability to predict hydrological variables and can be used in future research.

Keywords: precipitation forecasting; soft computing models; flow regime algorithm; multilayer perceptron neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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