期刊名称：Journal of Theoretical and Applied Computer Science
印刷版ISSN：2299-2634
电子版ISSN：2300-5653
出版年度：2012
卷号：6
期号：2
页码：3-12
出版社：Polska Akademia Nauk * Oddzial w Gdansku, Komisja Informatyki,Polish Academy of Sciences, Gdansk Branch, Computer Science Commission
摘要：Accurate forecast of rainfall has been one of the most important issues in hydrological research.
Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel
forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using
the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve
rainfall forecasting performance coupled with appropriate data–preprocessing techniques
by Singular Spectrum Analysis (SSA) and Partial Least Square (PLS) regression. In the process of
modular modeling, SSA is applied for the time series extraction of complex trends and structure
finding. In the second stage, the data set is divided into different training sets by Bagging and
Boosting technology. In the third stage, the modular RBF–NN predictors are produced by a different
kernel function. In the fourth stage, PLS technology is used to choose the appropriate number
of neural network ensemble members. In the final stage, least squares support vector regression
is used for ensemble of the M–RBF–NN to prediction purpose. The developed RBF–NN model
is being applied for real time rainfall forecasting and flood management in Liuzhou, Guangxi.
Aimed at providing forecasts in a near real time schedule, different network types were tested with
the same input information. Additionally, forecasts by M–RBF–NN model were compared to the
convenient approach. Results show that the predictions made using the M–RBF–NN approach are
consistently better than those obtained using the other method presented in this study in terms of
the same measurements. Sensitivity analysis indicated that the proposed M-RBF-NN technique
provides a promising alternative to rainfall prediction
关键词：Singular Spectrum Analysis; Radial Basis Function Neural Network; Partial Least Square Regression;
Rainfall prediction; Least Squares Support Vector Regression
