基于WPT-IDBO-RELM和WPT-IDMO-RELM模型的日径流预测  

Daily runoff prediction based on WPT-IDBO-RELM and WPT-IDMO-RELM models

作　　者：李菊[1] 崔东文 LI Ju;CUI Dongwen(College of Urban Construction,Yunnan Open University,Kunming 650500,China;Wenshan Water Bureau of Yunnan Province,Wenshan 663000,China)

机构地区：[1]云南开放大学城市建设学院,云南昆明650500 [2]云南省文山州水务局,云南文山663000

出　　处：《水利水电科技进展》2024年第6期48-55,85,共9页Advances in Science and Technology of Water Resources

基　　金：云南省教育厅科学研究基金项目(2023J0797,2024J0756)。

摘　　要：为提高日径流时间序列预测精度,改进正则化极限学习机(RELM)的预测性能,对比验证改进蜣螂优化(IDBO)算法和改进侏獴优化(IDMO)算法与其他算法的优化效果,提出了基于小波包变换(WPT)的WPT-IDBO-RELM和WPT-IDMO-RELM日径流时间序列预测模型。对云南省暮底河水库、马鹿塘电站入库日径流进行预测,结果表明WPT-IDBO-RELM和WPT-IDMO-RELM模型对暮底河水库日径流预测的平均绝对百分比误差分别为1.048%、1.015%,对马鹿塘电站日径流预测的平均绝对百分比误差分别为1.493%、1.478%,优于其他对比模型;IDBO、IDMO算法对标准测试函数和实例目标函数的寻优效果均优于其他对比算法,且IDBO、IDMO算法优化效果越好,RELM超参数越优,WPT-IDBO-RELM、WPT-IDMO-RELM模型预测精度越高;WPT可将日径流序列分解为分量更少、规律性更强的子序列分量,在提高预测精度的同时显著降低模型复杂度和计算规模。To improve the accuracy of daily runoff time series prediction and improve the prediction performance of the regularized extreme learning machine(RELM),the optimization performance of the improved dung beetle optimization(IDBO)algorithm and improved dwarf mongoose optimization(IDMO)algorithm was compared and verified,and the WPT-IDBO-RELM and WPT-IDMO-RELM models for daily runoff time series prediction were proposed based on wavelet packet transform(WPT).The daily inflows of the Mudihe Reservoir and Malutang Power Station in Yunnan Province were predicted.The results show that the average absolute percentage errors of the WPT-IDBO-RELM and WPT-IDMO-RELM models in predicting daily runoff for the Mudihe Reservoir are 1.048%and 1.015%,respectively,and 1.493%and 1.478%for Malutang Power Station,which are better than other comparative models.The optimization performance of the IDBO and IDMO algorithms on standard test functions and instance objective functions is better than that of comparative algorithms.The better the optimization performance of the IDBO and IDMO algorithms,the better the hyperparameters of the RELM,and the higher the prediction accuracy of the WPT-IDBO-RELM and WPT-IDMO-RELM models.WPT can decompose the daily runoff series into subseries components that have stronger regularity and are fewer in number,significantly reducing model complexity and computational scale while improving prediction accuracy.

关 键 词：日径流预测 正则化极限学习机 改进蜣螂优化算法 改进侏獴优化算法 小波包变换 

分 类 号：TV124[水利工程—水文学及水资源]