Simulating Jinping-Ⅰ Reservoir Operation Using the Support Vector Regression and the Long and Short Term Memory Algorithm

Reservoir operation is an important measure to realize the comprehensive benefit of the reservoir and to mitigate the adverse environmental impacts of water conservancy projects. It is crucial to full play the role of reservoir operation that develop a reasonable and effective reservoir operating plan. In this study, we explore explore the superiority of the LSTM model over traditional AI models in assisting reservoir operations, and Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) are employed to compare their capabilities for predicting outflows for Jinping first stage (JP) reservoir in China. Results show (1) the SVR and LSTM models are capable of providing reservoir outflows with satisfactory statistics, and the applicability of LSTM model in reservoir operation is verified; (2) comparing the two models, the results obtained by LSTM have the optimal statistical performances compared with SVR.