三峡大学学报（自然科学版）2025，Vol.47Issue(2)：26-32,7.DOI:10.13393/j.cnki.issn.1672-948X.2025.02.004
基于EWT-EVO/CDO-GPR模型的三峡入库月径流预测
Monthly Runoff Prediction of Three Gorges Reservoir Based on EWT-EVO/CDO-GPR Model
摘要
Abstract
To improve the accuracy of monthly runoff prediction for the Three Gorges reservoir,a Gaussian process regression(GPR)prediction model based on empirical wavelet transform(EWT),energy valley optimization(EVO)algorithm and Chernobyl disaster optimization(CDO)algorithm is proposed.Firstly,EWT is used to decompose the monthly runoff time series into trend term,periodic term and fluctuation term.Then the principle of EVO and CDO algorithms is briefly introduced,and GPR parameters are optimized by using EVO and CDO.Finally,the EWT-EVO-GPR and EWT-CDO-GPR models are established to predict the monthly runoff components by using the optimized super-parameters,and the final prediction results are obtained after reconstruction.The EWT-PSO-GPR and EWT-GA-GPR models based on particle swarm optimization(PSO)algorithm and genetic algorithm(GA)optimization,EWT-EVO-SVM,EWT-CDO-SVM,EWT-EVO-BP,EWT-CDO-BP models based on support vector machine(SVM)and BP neural network,and the non-optimized EWT-GPR model are constructed,WT-EVO-GPR and WT-CDO-GPR models based on wavelet transform(WT),EMD-EVO-GPR and EMD-CDO-GPR models based on empirical mode decomposition(EMD)and non-decomposed EVO-GPR and CDO-GPR models are compared and analyzed,and the models are verified by the monthly runoff time series data of the Three Gorges from 2009 to 2022.The results show that:The average absolute percentage errors of EWT-EVO-GPR and EWT-CDO-GPR models for the monthly runoff prediction of the Three Gorges reservoir are 0.689%and 0.699%respectively,and the determination coefficients are 0.9999,which are better than other comparison models,with higher prediction accuracy and better generalization ability;EWT takes the advantages of WT and EMD into account.It can decompose the monthly runoff time series data into more regular modal components,significantly improving the model prediction accuracy,and the decomposition effect is better than WT and EMD;EVO and CDO can effectively optimize GPR parameters,improve GPR prediction performance,and the optimization effect is better than PSO and GA;GPR performs well in dealing with complex regression problems such as high-dimensional,small sample,non-linear,and its prediction performance is superior to SVM and BP network.关键词
月径流预测/高斯过程回归/能量谷优化算法/切尔诺贝利灾难优化算法/经验小波变换/三峡Key words
monthly runoff forecast/gaussian process regression(GPR)/energy valley optimization algorithm(EVO)/chernobyl disaster optimization algorithm(CDO)/empirical wavelet transform(EWT)/Three Gorges分类
水利科学引用本文复制引用
徐荣华,崔东文..基于EWT-EVO/CDO-GPR模型的三峡入库月径流预测[J].三峡大学学报（自然科学版）,2025,47(2):26-32,7.基金项目
云南省创新团队建设专项(2018HC024) （2018HC024）
云南重点研发计划(科技入滇专项) （科技入滇专项）
国家澜湄合作基金项目(2018-1177-02) （2018-1177-02）
