长江科学院院报2025，Vol.42Issue(10)：24-31,53,9.DOI:10.11988/ckyyb.20240905
基于多种混合模型的中长期水文预报研究
Mid-Long Term Hydrological Forecasting Based on Multiple Hybrid Models
摘要
Abstract
[Objective]Improving the prediction accuracy of medium-and long-term hydrological forecast is of great significance for water resources scheduling,flood control and drought relief,and agricultural production.This study aims to select reliable,efficient,and practical hybrid machine learning models to improve forecasting per-formance for highly irregular,complex nonlinear,and multi-scale variable medium-and long-term hydrological forecasts,providing new approaches for enhancing hydrological forecast accuracy in changing environments.[Methods]To improve the accuracy of hydrological forecasts,based on the measured monthly runoff series at Wanxian Station in the Three Gorges Reservoir area,the mutual information method was used to screen forecasting factors.Then,Long Short-Term Memory(LSTM)models optimized by the Whale Optimization Algorithm(WOA),Grasshopper Optimization Algorithm(GOA),and Sparrow Search Algorithm(SSA)were established.Combined with Time-Varying Filtered Empirical Mode Decomposition(TVF-EMD),Complementary Ensemble Empirical Mode Decomposition(CEEMDAN),and Variational Mode Decomposition(VMD),multiple hybrid prediction models were established.Their prediction performance was evaluated using five indicators:mean absolute error(MAE),root mean square error(RMSE),Nash-Sutcliffe efficiency coefficient(NSE),mean absolute percentage error(MAPE),and correlation coefficient(R).[Results]The forecast factor scheme selected by the mutual in-formation method provided optimal model input,with a lag of 15 months achieving the maximum mutual information value and minimum MASE,representing the best input configuration.Among the three single machine learning models,LSTM and SVM outperformed BP,with LSTM and SVM showing similar performance.LSTM was preferred due to its sensitivity to temporal sequences,enabling better handling of nonlinear runoff prediction,and was thus used in coupling with different methods for runoff forecasting.The hybrid models following the"decompose-recon-struct"strategy outperformed single LSTM models:the VMD-LSTM model improved the NSE of the test set by 0.12 compared with the single LSTM model,exceeding CEEMDAN-LSTM and TVF-EMD-LSTM.Further integration with robust optimization algorithms enhanced accuracy:the VMD-SSA-LSTM model outperformed VMD-LSTM,VMD-GOA-LSTM,and VMD-WOA-LSTM,showing superior adaptability,generalization,and overall predictive perform-ance.[Conclusions]Machine learning models provide effective runoff forecasting methods for regions with limited hydrological and meteorological data.The approach of combining forecasting factor screening,data preprocessing,and integrating robust optimization algorithms with the model can further improve the accuracy of a single hydrologi-cal forecasting model.The established VMD-SSA-LSTM model achieved test period performance of MAE=32.65,RMSE=43.44,NSE=0.95,MAPE=12.9％,and R=0.98,representing the highest accuracy among compared models.This model meets practical production and daily life requirements and can provide a reference for water re-source management and industrial and agricultural production in the studied basin.关键词
中长期水文预报/互信息方法/麻雀搜索算法/变分模态分解/三峡库区Key words
medium-and long-term hydrological forecast/mutual information method/sparrow search algorithm/variational mode decomposition/Three Gorges Reservoir area分类
建筑与水利引用本文复制引用
魏兴,陈蒙恩,周育琳,冉立波,史瑞博,邹建华..基于多种混合模型的中长期水文预报研究[J].长江科学院院报,2025,42(10):24-31,53,9.基金项目
重庆市自然科学基金项目(CSTB2022NSCQ-MSX1392,CSTB2023NSCQ-MSX0694) （CSTB2022NSCQ-MSX1392,CSTB2023NSCQ-MSX0694）
重庆市教委科学技术研究项目(KJQN202301209) （KJQN202301209）
重庆市万州区科技创新项目(wzstc20230313) （wzstc20230313）
重庆市水利科技项目(CQSLK-2024023) （CQSLK-2024023）
