文章基本信息

标题：Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
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作者：Soo See Chai ; Wei Keat Wong ; Kok Luong Goh 等
期刊名称：International Journal of Environmental Science and Development
印刷版ISSN：2010-0264
出版年度：2017
卷号：8
期号：5
页码：385-388
DOI：10.18178/ijesd.2017.8.5.982
摘要：Abstract Rainfall is often defined by stochastic process due to its random characteristics, i.e. space and time dependent and it is therefore, not easy to predict. In general, rainfall is a highly non-linear and complicated phenomenon. In order to acquire an accurate prediction, advanced computer modeling and simulation is required. Artificial Neural Network (ANN) has been successfully used to predict the behavior of such non-linear system. Among the different types of ANN models used, Backpropagation Network (BPN) and Radial Basis Function Networks (RBFN) are the two common ANN models that had produced valuable results. However, there was no study conducted to research on which, among these two methods, is the better model for rainfall forecast. Therefore, this study will fill this gap by comparing the capabilities of these two ANN models in rainfall forecast using metrological data from year 2009 to 2013 obtained from Malaysian Meteorological Department for Kuching, Sarawak, Malaysia. From the research, it is concluded that, BPN (MSE≈0.16, R≈0.86) performs better as compared to RBFN (MSE≈0.22, R≈0.82). The strengths and weaknesses of these models are also presented in this paper. .
关键词：Artificial neural network; backpropagation; radial basis function; rainfall; classification