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

Author

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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
    United Arab Emirates University)

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.

Suggested Citation

  • Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:15:d:10.1007_s11269-022-03339-2
    DOI: 10.1007/s11269-022-03339-2
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    References listed on IDEAS

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    1. Mohammad Zounemat-Kermani & Abdollah Ramezani-Charmahineh & Reza Razavi & Meysam Alizamir & Taha B.M.J. Ouarda, 2020. "Machine Learning and Water Economy: a New Approach to Predicting Dams Water Sales Revenue," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1893-1911, April.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    4. Rana Muhammad Adnan & Xiaohui Yuan & Ozgur Kisi & Muhammad Adnan & Asif Mehmood, 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4469-4486, November.
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    1. Subbarayan Saravanan & Nagireddy Masthan Reddy & Quoc Bao Pham & Abdullah Alodah & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi, 2023. "Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset," Sustainability, MDPI, vol. 15(16), pages 1-26, August.

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