集成机器学习方法在水文预报中的应用与效果分析OA

To explore the application of integrated machine learning methods in hydrological forecasting,particularly for improving runoff prediction accuracy,this study compares single models(such as Random Forest,XGBoost,and LSTM)with integrated models(such as Bagging,Boosting,and Stacking)in both long-term and short-term forecasting.Multi-source data were used for model training and validation,and the prediction accuracy and stability of each model were evaluated.The results show that integrated methods,especially Stacking and Boosting,perform well in both long-term and short-term forecasting,particularly in extreme flood events and complex hydrological conditions,where their performance exceeds that of single models.The findings provide more precise and reliable technical support for hydrological forecasting,with significant practical value for flood control scheduling and water resource management.