中国农村水利水电Issue(4)：107-115,9.DOI:10.12396/znsd.2500785
基于18种核函数映射的孪生回归支持向量机月径流预测
Twin Regression Support Vector Machine Monthly Runoff Prediction Based on 18 Kernel Function Mappings
摘要
Abstract
The reasonable selection of kernel functions and kernel function parameters is of great significance for improving the performance of Twin Support Vector Regression(TWSVR).To improve the prediction accuracy of monthly runoff time series and compare and verify the effectiveness of TWSVR with different kernel function mappings,Wavelet Packet Transform(WPT),18 kernel functions(such as linear kernel function),Superb Fairy-wren Optimization Algorithm(SFOA),and TWSVR were used to propose the WPT-SFOA-TWSVR model with 18 kernel function mappings.Five common kernel function mappings of WPT-SFOA-SVR models were constructed for comparative analysis.A total of 23 models were validated through monthly runoff prediction examples at the Dishui,Nandong,Mengda,and Nankanghe hydrological stations in Yunnan Province.Firstly,WPT is used to decompose and process the monthly runoff time-series data of the instance,dividing it into a training set and a validation set.Then,SFOA is applied to optimize the TWSVR/SVR hyperparameters of different kernel function mappings.Finally,using the optimal hyperparameters,a WPT-SFOA-TWSVR/SVR model with different kernel function mappings was established to train,predict,and reconstruct each component of monthly runoff for the four instances.The results show that:① The WPT-SFOA-TWSVR model based on linear kernel function,Gaussian kernel function,polynomial kernel function,wavelet kernel function,Sigmoid kernel function,and neural kernel function mapping has the smallest prediction error and the best performance.The WPT-SFOA-TWSVR model based on ANOVA kernel function,Bessel kernel function,logarithmic kernel function,multiple quadratic kernel function,and power-law kernel function mapping follows closely.The WPT-SFOA-TWSVR model based on T-Student kernel function,Cauchy kernel function,and rational quadratic kernel function mapping has relatively larger prediction errors;The WPT-SFOA-TWSVR model based on Laplace kernel function,Fourier kernel function,chi square kernel function,and spherical kernel function mapping has the largest prediction error.② Under the same WPT decomposition and SFOA optimization conditions,the TWSVR model performs significantly better than SVR.③ Optimizing TWSVR hyperparameters using SFOA can significantly improve model performance and computational efficiency.④ The WPT-SFOA-TWSVR model with different kernel function mappings has good universality,providing reference and inspiration for the selection and optimization of TWSVR kernel functions.关键词
月径流预测/小波包变换/壮丽细尾鹩莺优化算法/核函数/孪生回归支持向量/超参数优化Key words
monthly runoff forecast/Wavelet Packet Transform(WPT)/Superb Fairy-wren Optimization Algorithm(SFOA)/kernel function/Twin Support Vector Regression(TWSVR)/hyperparameter optimization分类
建筑与水利引用本文复制引用
周正道,崔东文..基于18种核函数映射的孪生回归支持向量机月径流预测[J].中国农村水利水电,2026,(4):107-115,9.基金项目
滇池湖泊生态系统云南省野外科学观测研究站项目(202305AM340008) （202305AM340008）
国家自然科学基金项目(41702278) （41702278）
大理州基础研究科技项目(20232901A020002). （20232901A020002）
