Forecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenarios - Ibrahim K.S.M.H.; Huang Y.F.; Ahmed A.N.; Koo C.H.; El-Shafie A. (Springer, 2024)
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