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 mete...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28130/ http://umpir.ump.edu.my/id/eprint/28130/1/Rainfall%20intensity%20forecast%20using%20ensemble%20artificial%20neural%20network%20.pdf |
| _version_ | 1848822973220257792 |
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| author | Noor Zuraidin, Mohd Safar Ndzi, David Hairulnizam, Mahdin Ku Muhammad Naim, Ku Khalif |
| author_facet | Noor Zuraidin, Mohd Safar Ndzi, David Hairulnizam, Mahdin Ku Muhammad Naim, Ku Khalif |
| author_sort | Noor Zuraidin, Mohd Safar |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:49:45Z |
| format | Conference or Workshop Item |
| id | ump-28130 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:49:45Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-281302020-04-16T13:25:52Z http://umpir.ump.edu.my/id/eprint/28130/ Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate Noor Zuraidin, Mohd Safar Ndzi, David Hairulnizam, Mahdin Ku Muhammad Naim, Ku Khalif QA Mathematics T Technology (General) 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. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28130/1/Rainfall%20intensity%20forecast%20using%20ensemble%20artificial%20neural%20network%20.pdf Noor Zuraidin, Mohd Safar and Ndzi, David and Hairulnizam, Mahdin and Ku Muhammad Naim, Ku Khalif (2020) Rainfall intensity forecast using ensemble artificial neural network and data fusion for tropical climate. In: Recent Advances on Soft Computing and Data Mining. Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020) , 22-23 January 2020 , Melaka, Malaysia. pp. 241-250., 978. ISBN 978-3-030-36056-6 (Published) https://doi.org/10.1007/978-3-030-36056-6_24 |
| spellingShingle | QA Mathematics T Technology (General) Noor Zuraidin, Mohd Safar Ndzi, David Hairulnizam, Mahdin Ku Muhammad Naim, Ku Khalif 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 | QA Mathematics T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/28130/ http://umpir.ump.edu.my/id/eprint/28130/ http://umpir.ump.edu.my/id/eprint/28130/1/Rainfall%20intensity%20forecast%20using%20ensemble%20artificial%20neural%20network%20.pdf |