节水灌溉Issue(3)：48-54,7.DOI:10.12396/jsgg.2025291
VMD-LSTM-FEDformer降水预测融合模型研究
Research on the Precipitation Prediction Fusion Model Based on VMD-LSTM-FEDformer
摘要
Abstract
In recent years,the frequency of extreme rainfall events has increased,making it particularly urgent to improve the accuracy of precipitation prediction to support crop growth.Leveraging the advantages of Variational Mode Decomposition(VMD)in time series decomposition,and the complementary strengths of Long Short-Term Memory(LSTM)networks—which excel in handling local temporal information,and FEDformer,which is adept at processing global dependencies and possesses frequency domain characteristics,this paper proposes a precipitation prediction method based on a combined VMD-LSTM-FEDformer model.Five meteorological stations with different geographical features in Henan Province were selected for prediction analysis.The results indicated that the prediction errors of the combined model at all meteorological stations were within 5 mm,demonstrating the strong robustness of the model.In comparative experiments,the Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Coefficient of Determination(R2)of the VMD-LSTM-FEDformer combined model were 9.339 8 mm,12.703 5 mm,and 0.964 4,respectively.These metrics outperformed those of other models,proving that the proposed model possesses excellent predictive capability and practical application value.关键词
时间序列预测/变分模态分解/FEDformer/降水预测/融合模型Key words
time series prediction/variational mode decomposition/FEDformer/precipitation prediction/fusion model分类
农业科技引用本文复制引用
薛院红,宋文广,江琼琴,尹鸿飞,王润辰..VMD-LSTM-FEDformer降水预测融合模型研究[J].节水灌溉,2026,(3):48-54,7.基金项目
国家科技重大专项(2021DJ1006) （2021DJ1006）
广东海洋大学科研启动基金项目(YJR24010) （YJR24010）
中国高校研究创新基金(2023ZY010). （2023ZY010）
