水力发电2025，Vol.51Issue(3)：22-27,118,7.
耦合多变量筛选和多层LSTM的短期径流预测研究
Study on Short-term Runoff Prediction Coupled with Multivariate Screening and Multilayer LSTM
摘要
Abstract
The screening of influencing factors for runoff prediction is a key link in the process of basin water forecasting.When building a short-term reservoir runoff prediction model with complex time series process,there are a variety of influencing factors that can be input into the model.In order to reduce the dimensions of input dataset and verify the new key influencing factors,this paper takes the short-term reservoir runoff prediction as the research object,and establishes the long short-term memory(LSTM)neural network of different scale datasets for model calibration.Then the Fisher Score algorithm and entropy weight-TOPSIS method are introduced to select seven key influencing factors from sixteen conventional influencing factors related to hydrometeorology,reservoir scheduling and power generation scheduling,and the root mean square error(RMSE)is used as accuracy index to optimize the hyperparameters of the three LSTM models.Finally the parameters and influencing factors of each optimized screening are superimposed into the multi-layer LSTM model to verify the flow prediction of the new key influencing factors.It is found that the LSTM model after screening the influencing factors has a better calibration effect,and the newly proposed influencing factor of the deviation rate of the execution of upstream reservoir power generation plan can further improve the prediction accuracy of reservoir runoff.关键词
长短时记忆/径流预测/Fisher Score算法/水库调度/发电计划执行偏差率/关键影响因子Key words
long short-term memory/runoff prediction/Fisher Score algorithm/reservoir scheduling/deviation rate of power generation plan execution/key influencing factor分类
地球科学引用本文复制引用
田伟,殷兆凯,董义阳,黄迪,刘青..耦合多变量筛选和多层LSTM的短期径流预测研究[J].水力发电,2025,51(3):22-27,118,7.基金项目
国家重点研发计划项目(2022YFC3002702) （2022YFC3002702）
