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

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.