Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting

Daily inflow forecasting is of vital importance in reservoir economic operation. In the context hydrometeorological forecasting, effectiveness data-driven models has been demonstrated as bias correctors for physically-based or direct models. However, existing studies only highlight performance improvements provided by model, lacking a comprehensive investigation on whether model should be used This study constructs long short-term memory (LSTM)-based preprocessing and postprocessing techniques hydrological which are tested linear scaling autoregressive (AR) The integrated compared with LSTM-only model. Shuibuya Zuojiang reservoirs China selected case studies. Results indicate that: (1) LSTM-based effective both (2) comparable to when trained four more years data, while it better than less data. These findings demonstrate that methods can effectively correct output, integrating physical useful improving multi-step ahead if limited data obtained.