水利水电科技进展2025，Vol.45Issue(3)：62-69,8.DOI:10.3880/j.issn.1006-7647.2025.03.009
基于多模型融合的椒江流域季节性径流集合预报
Ensemble forecasting of seasonal streamflow in the Jiaojiang River Basin based on multi-model fusion
摘要
Abstract
In order to enhance the predictability of seasonal streamflow,numerical weather prediction was coupled with the Xin'anjiang model,the distributed hydrological soil vegetation model(DHSVM),and the long short-term memory model(LSTM)for ensemble forecasting of monthly streamflow in the Jiaojiang River Basin from 2012 to 2020.Three different methods,namely,equal weighting,unequal weighting,and BP neural network-based weighting,were employed to fuse the outputs from the three models.Comparison was made between the fused forecasts and the optimal forecasts of single models.The results indicate that the BP neural network fusion method significantly enhances the forecasting accuracy,demonstrating superior performance over other methods.Notably,this method substantially extends the effective forecast lead time across all four distinct seasons(spring,summer,autumn,and winter),thereby providing more reliable hydrological predictions for water resources management and utilization in the basin.关键词
季节性径流预报/新安江模型/DHSVM模型/LSTM模型/多模型融合/椒江流域Key words
seasonal streamflow forecasting/Xin'anjiang model/DHSVM model/LSTM model/multi-model fusion/the Jiaojiang River Basin分类
水利科学引用本文复制引用
周鹏,许月萍,周欣磊,刘莉,梁霄,郭玉雪..基于多模型融合的椒江流域季节性径流集合预报[J].水利水电科技进展,2025,45(3):62-69,8.基金项目
国家重点研发计划项目(2021YFD1700802) （2021YFD1700802）
浙江省自然科学基金重点项目(LZ20E090001) （LZ20E090001）
