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

• Main Authors: Ibrahim K.S.M.H., Huang Y.F., Ahmed A.N., Koo C.H., El-Shafie A.
• Other Authors: 57225749816
• Format: Article
• Published: Springer 2024
• Subjects: Adaptive neuro-fuzzy inference system (ANFIS); Extreme Gradient Boosting (XG-Boost); Grid Search optimizer; Hyper-parameters; Inflow Forecast; Machine learning; Multilayer Perceptron neural network (MLPNN); Support Vector Regression (SVR); Adaptive boosting; Decision making; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Multilayer neural networks; Multilayers; Reservoirs (water); Water resources; Adaptive neuro-fuzzy inference; Adaptive neuro-fuzzy inference system; Extreme gradient boosting (XG-boost); Gradient boosting; Grid search; Grid search optimizer; Hyper-parameter; Inflow forecast; Machine-learning; Multilayer perceptron neural network; Multilayers perceptrons; Neuro-fuzzy inference systems; Perceptron neural networks; Search optimizer; Support vector regression; Support vector regressions

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 R� of 0.5