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

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...

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Main Authors: Chai, Soo See, Wong, Wei Keat, Kok, Luong Goh
Format: Article
Language:English
Published: University of Queensland, Australia 2017
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/15861/1/Rainfall%20Classification%20for%20Flood%20Prediction%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/15861/
http://www.ijesd.org/
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spelling my.unimas.ir.158612022-09-29T03:08:34Z http://ir.unimas.my/id/eprint/15861/ Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network Chai, Soo See Wong, Wei Keat Kok, Luong Goh T Technology (General) 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. University of Queensland, Australia 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/15861/1/Rainfall%20Classification%20for%20Flood%20Prediction%20%28abstract%29.pdf 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 http://www.ijesd.org/ doi: 10.18178/ijesd.2017.8.5.982
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Chai, Soo See
Wong, Wei Keat
Kok, Luong Goh
Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
description 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.
format Article
author Chai, Soo See
Wong, Wei Keat
Kok, Luong Goh
author_facet Chai, Soo See
Wong, Wei Keat
Kok, Luong Goh
author_sort Chai, Soo See
title Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
title_short Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
title_full Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
title_fullStr Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
title_full_unstemmed Rainfall Classification for Flood Prediction Using Meteorology Data of Kuching, Sarawak, Malaysia: Backpropagation vs Radial Basis Function Neural Network
title_sort rainfall classification for flood prediction using meteorology data of kuching, sarawak, malaysia: backpropagation vs radial basis function neural network
publisher University of Queensland, Australia
publishDate 2017
url http://ir.unimas.my/id/eprint/15861/1/Rainfall%20Classification%20for%20Flood%20Prediction%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/15861/
http://www.ijesd.org/
_version_ 1745566032493281280
score 13.251813

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