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Forecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenarios

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Abstract

Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R² of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models.

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full data may not be disclosed as it belongs to Tenaga National Berhad which identify the used data as a matter of national security.

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Acknowledgements

The research was supported by the Ministry of Higher Education (MoHE) Malaysia through Fundamental Research Grant Scheme (FRGS) project FRGS/1/2018/TK01/UTAR/02/2. We also want to thank Universiti Tunku Abdul Rahman (UTAR) through UTAR Research Fund Top-up Scheme under the vote number of 6235/H02. Furthermore, we would extend our deepest gratitude to Lembaga Urus Air Selangor (LUAS) and the Department of Irrigation and Drainage (JPS) Malaysia for supplying us with the data used in this research.

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  1. Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Bandar Sungai Long, 43200, Kajang, Selangor, Malaysia

    Karim Sherif Mostafa Hassan Ibrahim, Yuk Feng Huang & Chai Hoon Koo

  2. Institute of Energy Infrastructure, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia

    Ali Najah Ahmed

  3. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Ahmed El-Shafie

  4. National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates

    Ahmed El-Shafie

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Ibrahim, K.S.M.H., Huang, Y.F., Ahmed, A.N. et al. Forecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenarios. Appl Intell 53, 10893–10916 (2023). https://doi.org/10.1007/s10489-022-04029-7

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