Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate
This paper proposes an ensemble method based on neural network architecture and stacking generalization. The objective is to develop a novel ensemble of Artificial Neural Network models with back propagation network and dynamic Recurrent Neural Network to improve prediction accuracy. Historical...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2020
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| Online Access: | http://eprints.uthm.edu.my/3421/1/KP%202020%20%2871%29.pdf http://eprints.uthm.edu.my/3421/ https://doi.org/10.1007/978-3-030-36056-6_24 |
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| _version_ | 1833417332052983808 |
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| author | Mohd Safar, Noor Zuraidin Ndzi, David Mahdin, Hairulnizam Ku Khalif, Ku Muhammad Naim |
| author_facet | Mohd Safar, Noor Zuraidin Ndzi, David Mahdin, Hairulnizam Ku Khalif, Ku Muhammad Naim |
| author_sort | Mohd Safar, Noor Zuraidin |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper proposes an ensemble method based on neural network
architecture and stacking generalization. The objective is to develop a novel
ensemble of Artificial Neural Network models with back propagation network
and dynamic Recurrent Neural Network to improve prediction accuracy. Historical
meteorological parameters and rainfall intensity have been used for
predicting the rainfall intensity forecast. Hourly predicted rainfall intensity
forecast are compared and analyzed for all models. The result shows that for 1 h
of prediction, the neural network ensemble forecast model returns 94% of
precision value. The study achieves that the ensemble neural network model
shows significant improvement in prediction performance as compared to the
individual neural network model. |
| format | Conference or Workshop Item |
| id | my.uthm.eprints-3421 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2020 |
| record_format | eprints |
| spelling | my.uthm.eprints-34212021-11-02T03:18:17Z http://eprints.uthm.edu.my/3421/ Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate Mohd Safar, Noor Zuraidin Ndzi, David Mahdin, Hairulnizam Ku Khalif, Ku Muhammad Naim TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television This paper proposes an ensemble method based on neural network architecture and stacking generalization. The objective is to develop a novel ensemble of Artificial Neural Network models with back propagation network and dynamic Recurrent Neural Network to improve prediction accuracy. Historical meteorological parameters and rainfall intensity have been used for predicting the rainfall intensity forecast. Hourly predicted rainfall intensity forecast are compared and analyzed for all models. The result shows that for 1 h of prediction, the neural network ensemble forecast model returns 94% of precision value. The study achieves that the ensemble neural network model shows significant improvement in prediction performance as compared to the individual neural network model. 2020 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/3421/1/KP%202020%20%2871%29.pdf Mohd Safar, Noor Zuraidin and Ndzi, David and Mahdin, Hairulnizam and Ku Khalif, Ku Muhammad Naim (2020) Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate. In: Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), 22-23 January 2020, Melaka, Malaysia. (Submitted) https://doi.org/10.1007/978-3-030-36056-6_24 |
| spellingShingle | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Mohd Safar, Noor Zuraidin Ndzi, David Mahdin, Hairulnizam Ku Khalif, Ku Muhammad Naim Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate |
| title | Rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| title_full | Rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| title_fullStr | Rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| title_full_unstemmed | Rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| title_short | Rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| title_sort | rainfall intensity forecast using ensemble
artificial neural network and data fusion
for tropical climate |
| topic | TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
| url | http://eprints.uthm.edu.my/3421/1/KP%202020%20%2871%29.pdf http://eprints.uthm.edu.my/3421/ https://doi.org/10.1007/978-3-030-36056-6_24 |
| url_provider | http://eprints.uthm.edu.my/ |