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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fatemeh Barzegari Banadkooki & Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Wani M. Ridwam & Ahmed Sefelnasr & Ahmed El-Shafie, 2019. "Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6681-:d:290948
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    References listed on IDEAS

    as
    1. R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    2. Mohammad Ehteram & Hojat Karami & Sayed Farhad Mousavi & Saaed Farzin & Alcigeimes B. Celeste & Ahmad-El Shafie, 2018. "Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4681-4706, November.
    3. Mohammad Ehteram & Vijay P Singh & Ahmad Ferdowsi & Sayed Farhad Mousavi & Saeed Farzin & Hojat Karami & Nuruol Syuhadaa Mohd & Haitham Abdulmohsin Afan & Sai Hin Lai & Ozgur Kisi & M A Malek & Ali Na, 2019. "An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-25, May.
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    Cited by:

    1. Sarmad Dashti Latif & Ali Najah Ahmed & Edlic Sathiamurthy & Yuk Feng Huang & Ahmed El-Shafie, 2021. "Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 351-369, October.
    2. Yan Guo & Wei Tang & Guanghua Hou & Fei Pan & Yubo Wang & Wei Wang, 2021. "Research on Precipitation Forecast Based on LSTM–CP Combined Model," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    3. Mohammad Ehteram & Ali Najah Ahmed & Ming Fai Chow & Sarmad Dashti Latif & Kwok-wing Chau & Kai Lun Chong & Ahmed El-Shafie, 2023. "Optimal operation of hydropower reservoirs under climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10627-10659, October.
    4. NurIzzah M. Hashim & Norazian Mohamed Noor & Ahmad Zia Ul-Saufie & Andrei Victor Sandu & Petrica Vizureanu & György Deák & Marwan Kheimi, 2022. "Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models," Sustainability, MDPI, vol. 14(13), pages 1-23, June.
    5. Muhammad Waseem & Ijaz Ahmad & Ahmad Mujtaba & Muhammad Tayyab & Chen Si & Haishen Lü & Xiaohua Dong, 2020. "Spatiotemporal Dynamics of Precipitation in Southwest Arid-Agriculture Zones of Pakistan," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    6. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.

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