基于深度学习的三峡电站未来坝前最大最小水位预测OA北大核心CSTPCD

The maximum and minimum water levels are crucial constraints in the calculation of cascade reservoir op-erations and the economic operation of hydropower stations.Traditional iterative methods for multi-period predictions lack credibility due to error accumulation.This study employs a Long Short-Term Memory(LSTM)model which is effective in handling time series problems to predict the maximum and minimum water levels of the Three Gorges Reservoir over the next four days.Two LSTM-based deep learning models incorporating different characteristic varia-bles are developed,and a conventional forecast model based on the water balance framework is also constructed for comparison.Results demonstrate that the deep learning model,which considers the propagation law of water surface profiles in the Three Gorges Reservoir area,delivers accurate and stable predictions,achieving an absolute error of less than 40 cm for 99％of the predictions.