基于数据分解与斑马算法优化的混合核极限学习机月径流预测  

Monthly Runoff Prediction Using Hybrid Kernel Extreme Learning Machine Based on Data Decomposition and Zebra Algorithm Optimization

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

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

出　　处：《长江科学院院报》2024年第6期42-50,共9页Journal of Changjiang River Scientific Research Institute

基　　金：云南省教育厅教育科学研究基金项目(2023J0797);云南省水利厅水利科技项目(2024BC202003)。

摘　　要：为提高月径流预测精度,改进混合核极限学习机(HKELM)预测性能,提出小波包分解(WPT)-斑马优化算法(ZOA)-HKELM组合模型。利用WPT处理月径流时序数据,构建局部高斯径向基核函数和全局多项式核函数相混合的HKELM;通过ZOA优化HKELM超参数(正则化参数、核参数、权重系数),建立WPT-ZOA-HKELM组合模型,并构建WPT-遗传算法(GA)-HKELM、WPT-灰狼优化(GWO)算法-HKELM、WPT-鲸鱼优化算法(WOA)-HKELM、WPT-ZOA-极限学习机(ELM)、WPT-ZOA-最小二乘支持向量机(LSSVM)、ZOA-HKELM作对比模型,通过黑河流域莺落峡、讨赖河水文站月径流时间序列预测实例对各模型进行检验。结果表明:(1)莺落峡、讨赖河水文站月径流时间序列WPT-ZOA-HKELM模型预测的平均绝对百分比误差分别为1.054%、0.761%,决定系数均达0.999 9,优于其他对比模型,具有更高的预测精度,预测效果更好。(2)利用ZOA优化HKELM超参数,可提高HKELM预测性能,优化效果优于GWO、WOA、GA。(3)预测模型能充分发挥WPT、ZOA和HKELM优势,提高月径流预测精度;在相同分解和优化情形下,HKELM的预测性能优于ELM、LSSVM。In order to enhance the precision of monthly runoff forecasts and optimize the prediction performance of the Hybrid Kernel Extreme Learning Machine(HKELM),we propose a synergistic approach integrating Wavelet Packet Decomposition(WPT),the Zebra Optimization Algorithm(ZOA),and HKELM.The approach involves applying WPT to preprocess monthly runoff time series data and constructing a HKELM that combines local Gaussian radial basis function with global polynomial kernel function.By refining HKELM hyperparameters( including regularization parameters,kernel parameters,and weight coefficients) through ZOA,we establish the WPT-ZOAHKELM model,alongside comparative models such as WPT-Genetic Algorithm(GA)-HKELM,WPT-Grey Wolf Optimization(GWO) algorithm-HKELM,WPT-Whale Optimization(WOA)-HKELM,WPT-ZOA Extreme Learning Machine( ELM),WPT-ZOA Least Squares Support Vector Machine( LSSVM),and ZOA-HKELM.These models are evaluated using monthly runoff time series data from the Yingluoxia and Tuolai River hydrological stations in the Heihe River Basin.Our findings indicate that:(1) The WPT-ZOA-HKELM model achieves average absolute percentage errors of 1.054% and 0.761% respectively,with determination coefficients of 0.999 9,surpassing other comparative models in terms of prediction accuracy and performance.(2) Optimization of HKELM hyperparameters with ZOA enhances predictive performance compared to GWO,WOA,and GA.(3) Through leveraging WPT,ZOA,and HKELM,the prediction model significantly improves monthly runoff forecast accuracy.Under equivalent decomposition and optimization conditions,the predictive performance of HKELM is superior to ELM and LSSVM.

关 键 词：月径流预测 时间序列 斑马优化算法 混合核极限学习机 小波包变换 超参数优化 

分 类 号：TV124[水利工程—水文学及水资源] P333[天文地球—水文科学]