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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University of Queensland, Australia
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/15861/ http://ir.unimas.my/id/eprint/15861/1/Rainfall%20Classification%20for%20Flood%20Prediction%20%28abstract%29.pdf |
| _version_ | 1848837942029582336 |
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| 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 |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T06:47:40Z |
| format | Article |
| id | unimas-15861 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:47:40Z |
| publishDate | 2017 |
| publisher | University of Queensland, Australia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-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 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/15861/ http://ir.unimas.my/id/eprint/15861/ http://ir.unimas.my/id/eprint/15861/ http://ir.unimas.my/id/eprint/15861/1/Rainfall%20Classification%20for%20Flood%20Prediction%20%28abstract%29.pdf |