水利学报2025，Vol.56Issue(7)：933-944,12.DOI:10.13243/j.cnki.slxb.20240319
径流序列两段动态分解-预测-重构中长期预报模型
A two-stage dynamic decomposition-prediction-reconstruction model for medium-long term runoff forecasting
摘要
Abstract
Medium-long term runoff forecasting is a critical foundation for optimizing water resource management.With the intensification of climate change,the variability of runoff has increased,making the forecasting more challenging.To improve the accuracy of runoff forecasts and extend the forecast period,in this paper,by integrat-ed Seasonal and Trend decomposition using Loess(STL),Variational Mode Decomposition(VMD)and the deep learning model Informer,a two-stage dynamic decomposition-prediction-reconstruction model(STL-VMD-In-former)for medium-long term runoff forecast was development.The application at the Shixiali hydrological station of Yongding River Basin shows that the forecast Nash-Sutcliffe efficiency(NSE)reaches 0.897,0.843,and 0.796 for prediction periods of 1,3,and 6 months,respectively,indicating the proposed method with the potential in separating the time series,extending the forecasting horizon,and improving forecast accuracy.The approach will benefit to the medium-long term runoff forecasting.关键词
径流预报/周期趋势分解/变分模态分解/Informer/永定河Key words
runoff forecast/STL/VMD/Informer/Yongding River分类
天文与地球科学引用本文复制引用
陈小泽,王忠静,刘丹,石羽佳,KANG Boosik..径流序列两段动态分解-预测-重构中长期预报模型[J].水利学报,2025,56(7):933-944,12.基金项目
国家重点研发计划项目(2022YFE0101100) （2022YFE0101100）
国家自然科学基金项目(52300246,42307558) （52300246,42307558）
