Hybrid Support Vector Regression with Parallel Co-Evolution Algorithm Based on GA and PSO for Forecasting Monthly Rainfall

Jiansheng Wu,Yongsheng Xie

doi:10.4236/jsea.2019.1212032

Abstract

Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.

Highlights

IntroductionThe support vector machine (SVM) developed by Vapnik and his colleagues, is an important machine learning tool based on statistical learning theory, using the principle of structural risk minimization
The present study proposed a novel parallel co-evolution algorithm of Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO) to optimize the Support Vector Regression (SVR) parameters, namely SVRGAPSO based on the mechanism of information interaction between GA and PSO when they are iterating over two populations
These results show that the advantages of parallel co-evolutionary algorithm can guide individual which has been plunged into the local optimum value to deviate from the original local minima

Summary

Introduction

The support vector machine (SVM) developed by Vapnik and his colleagues, is an important machine learning tool based on statistical learning theory, using the principle of structural risk minimization. Because SVR is a specific type of learning algorithms, characterized by the capacity control of the decision function, the use of the kernel function and the sparsity of the solution, SVR has used on regression estimation, include monthly rainfall forecasting modelling. These unique characteristics of SVR make them a promising alternative approach to traditional regression estimation approaches

Methods

Results

Conclusion