The Model of Rainfall Forecasting by Support Vector Regression Based on Particle Swarm Optimization Algorithms
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel neural network technique, support vector regression (SVR), to monthly rainfall forecasting. The aim of this study is to examine the feasibility of SVR in monthly rainfall forecasting by comparing it with back-propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model. This study proposes a novel approach, known as particle swarm optimization (PSO) algorithms, which searches for SVRs optimal parameters, and then adopts the optimal parameters to construct the SVR models. The monthly rainfall in Guangxi of China during 1985-2001 were employed as the data set. The experimental results demonstrate that SVR outperforms the BPNN and ARIMA models based on the normalized mean square error (NMSE) and mean absolute percentage error (MAPE).
Particle Swarm Optimization Neural Network Support Vector Regression
Shian Zhao Lingzhi Wang
Department of Mathematics and Computer Science, Baise University Baise, 533000, Guangxi Department of Mathematics and Computer Science, Liuzhou Teachers College Liuzhou, 545004, Guangxi, C
国际会议
无锡
英文
110-119
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）