Application of Combined Multiple Linear Regression Model in Runoff Prediction - GUO Cunwen (Renmin Zhujiang)
In order to improve the hydrological prediction accuracy,a shuffled frog leaping algorithm (SFLA)-combined multiple linear regression (CMLR) runoff prediction model is proposed.This paper first builds a CMLR model based on principal component analysis (PCA) data with and without dimensionality reduction;then optimizes the CMLR constant term,partial regression coefficient and combined weight coefficient by the SFLA to establish a SFLA-CMLR runoff prediction model;finally apply the SFLA-CMLR model on two examples of annual runoff prediction,and establishes the SFLA-PCA-MLR,SFLA-PCA-SVM (support vector machine),(least squares) LS-PCA-MLR,PCA-SVM,with dimensionality reduction of PCA,as well as SFLA-MLR,SFLA-SVM,LS-MLR,SVM,without dimensionality reduction as comparative prediction models.The results show that the average relative error of the SFLA-CMLR model for the annual runoff prediction of the two examples is 1.54% and 4.63%,respectively,and the prediction accuracy is better than that of SFLA-PCA-MLR and other 8 models.Therefore,it has better prediction accuracy and generalization ability.