Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network

Chai, Soo See and Wong, Wei Keat and Kok, Luong Goh (2017) Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network. International Journal of Environmental Science and Development, 8 (5). ISSN 2010-0264

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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.

Item Type: Article
Uncontrolled Keywords: Artificial neural network, backpropagation, radial basis function, rainfall, classification, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 06 Apr 2017 04:55
Last Modified: 06 Apr 2017 04:55
URI: http://ir.unimas.my/id/eprint/15861

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