节水灌溉Issue(4)：42-48,57,8.DOI:10.12396/jsgg.2025361
基于GRA-Optuna-LSTM模型的泵站前池水位多步预测研究
Research on Multi-step Prediction of Water Level in the Pump Station Forebay Based on the GRA-Optuna-LSTM Model
摘要
Abstract
To improve the accuracy of water level forecasting of the pump station forebay and extend the forecasting horizon,this paper takes the Ningxia Guhai Irrigation Cascade Pump Station as the research object and proposes an LSTM prediction model that integrates Grey Relational Analysis(GRA)and the Optuna hyperparameter optimization method.Through GRA,four key influencing factors were identified:the flow rate of the Kuosan pump station,the water level in the outflow pool of the Kuoer pump station,the flow rate of the Kuoer pump station,and the flow difference between the two pump stations.Optuna was used to automatically optimize the LSTM hyperparameters,and the prediction results under different forecasting horizons(1,2,3 and 4 h)were compared with LSTM,Optuna-XGBoost,and Optuna-BP models.The results show that the Optuna-LSTM model has the lowest error metrics,significantly outperforming the comparative models,demonstrating superior accuracy and generalization capability,thus providing a high-precision and highly generalizable data-driven solution for short-term forecasting of pump station water levels.关键词
梯级泵站/泵站前池/水位预测/Optuna-LSTM模型/灰色关联分析Key words
step pump station/pump station forebay/water level prediction/Optuna-LSTM model/GRA分类
农业科技引用本文复制引用
贾莉,闫汝一,陈文婷,田福昌,吴怀雨..基于GRA-Optuna-LSTM模型的泵站前池水位多步预测研究[J].节水灌溉,2026,(4):42-48,57,8.基金项目
国家重点研发计划资助项目(2022YFC3202501) （2022YFC3202501）
固海扩灌扬水更新改造工程长距离梯级供水系统云泵站创新技术集成与成果总结(GKGX-KY-2024-003). （GKGX-KY-2024-003）
