水资源与水工程学报Issue(6)：231-235,5.DOI:10.11705/j.issn.1672-643X.2014.06.049
遗传算法与最小二乘支持向量机在年径流预测中的应用
Application of genetic algorithm and least squares support vector machine in prediction of annual runoff
代兴兰1
作者信息
- 1. 云南省水文水资源局曲靖分局，云南曲靖655000
- 折叠
摘要
Abstract
In order to overcome the lack of learning parameters of least square support vector machine ( LSSVM) which is chosen by human experience , the paper used the genetic algorithm ( GA) to select LSSVM penalty factor c and kernel parameter and construct GA -LSSVM annual runoff forecasting mod-el, and set up LSSVM , GA-BP and traditional BP model as the comparison .Taking Yunnan province river hydrological station annual runoff prediction as a case study , it used the data before 30 years and after 22 years to train and predict every model .The results show that the absolute value of the average relative error of annual runoff and the maximum absolute value of relative error predicted by GA -LSSVM model after 22 years are 3.13%, 8.66%, the prediction accuracy of GA-LSSVM model is better than that of LSSVM, GA-BP and traditional BP model .The global optimization ability of GA algorithm is strong .The LSSVM learning parameters gotten by optimization of GA algorithm can effectively improve the prediction accuracy of LSSVM model and the generalization ability .关键词
径流预测/遗传算法/最小二乘支持向量机/BP神经网络Key words
runoff forecast/genetic algorithm/least squares support vector machine/BP neural network分类
天文与地球科学引用本文复制引用
代兴兰..遗传算法与最小二乘支持向量机在年径流预测中的应用[J].水资源与水工程学报,2014,(6):231-235,5.
