By Topic

Application of Hybrid RBF Neural Network Ensemble Model Based on Wavelet Support Vector Machine Regression in Rainfall Time Series Forecasting

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lingzhi Wang ; Dept. of Math. & Comput. Sci., Liuzhou Teachers Coll., Liuzhou, China ; Jiansheng Wu

In this paper, a novel hybrid Radial Basis Function Neural Network (RBF-NN) ensemble model using Wavelet Support Vector Machine Regression (W-SVR) is developed for rainfall forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to the different individual RBF-NN models, and then various single RBF-NN predictors are produced based on diversity principle. In the third stage, the Partial Least Square (PLS) technology is used to choosing the appropriate number of neural network ensemble members. In the final stage, W-SVR is used for ensemble of the RBF-NN to prediction purpose. For testing purposes, this study compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of monthly rainfall forecasting on Guangxi, China. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed hybrid ensemble technique provides a promising alternative to rainfall prediction.

Published in:

Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on

Date of Conference:

23-26 June 2012