Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques

Yahia Mutalib Tofiq (), Sarmad Dashti Latif (), Ali Najah Ahmed (), Pavitra Kumar () and Ahmed El-Shafie ()
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Yahia Mutalib Tofiq: Universiti Tenaga Nasional (UNITEN)
Sarmad Dashti Latif: Komar University of Science and Technology
Ali Najah Ahmed: Universiti Tenaga Nasional (UNITEN)
Pavitra Kumar: University of Liverpool
Ahmed El-Shafie: Faculty of Engineering, University of Malaya

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 15, No 13, 5999-6016

Abstract: Abstract The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R2 (0.9012). The input combination for the optimum RF model was Qt-1, Qt-11, and Qt-12 (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management.

Keywords: Streamflow prediction; Aswan High Dam; Artificial Neural Network; Support Vector Machine; Random Forest; Boosted Tree Regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03339-2

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