Ensemble neural networks with input optimization for flood forecasting
Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113840/1/113840.pdf |
| _version_ | 1824452342861266944 |
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| author | Mohd Khairudin, Nazli Mustapha, Norwati Mohd Aris, Teh Noranis Zolkepli, Maslina |
| author_facet | Mohd Khairudin, Nazli Mustapha, Norwati Mohd Aris, Teh Noranis Zolkepli, Maslina |
| author_sort | Mohd Khairudin, Nazli |
| collection | UPM |
| description | Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. The aim is to highlight the improvement on accuracy of the forecast. Prior to the development of such ensemble model, data are optimized in two steps which are decomposed it using discrete wavelet transform (DWT) to reduce variations in the rainfall series and selecting dominant features using entropy called mutual information (MI) for the model. The result of the experiments indicates that ensemble neural networks model based on the data decomposition and entropy feature selection has outperformed individually executed forecast model in term of RMSE, MSE and NSE. This study proved that the proposed method has reduce the data variance and provide better forecasting with minimal error. With minimal forecast error the generalization of the model is improved. |
| first_indexed | 2025-02-19T02:49:00Z |
| format | Article |
| id | upm.eprints-113840 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2025-02-19T02:49:00Z |
| publishDate | 2024 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | upm.eprints-1138402025-01-14T06:54:49Z http://psasir.upm.edu.my/id/eprint/113840/ Ensemble neural networks with input optimization for flood forecasting Mohd Khairudin, Nazli Mustapha, Norwati Mohd Aris, Teh Noranis Zolkepli, Maslina Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. The aim is to highlight the improvement on accuracy of the forecast. Prior to the development of such ensemble model, data are optimized in two steps which are decomposed it using discrete wavelet transform (DWT) to reduce variations in the rainfall series and selecting dominant features using entropy called mutual information (MI) for the model. The result of the experiments indicates that ensemble neural networks model based on the data decomposition and entropy feature selection has outperformed individually executed forecast model in term of RMSE, MSE and NSE. This study proved that the proposed method has reduce the data variance and provide better forecasting with minimal error. With minimal forecast error the generalization of the model is improved. Institute of Advanced Engineering and Science 2024 Article PeerReviewed text en cc_by_nc_sa_4 http://psasir.upm.edu.my/id/eprint/113840/1/113840.pdf Mohd Khairudin, Nazli and Mustapha, Norwati and Mohd Aris, Teh Noranis and Zolkepli, Maslina (2024) Ensemble neural networks with input optimization for flood forecasting. Bulletin of Electrical Engineering and Informatics, 13 (5). pp. 3686-3694. ISSN 2089-3191; eISSN: 2302-9285 https://beei.org/index.php/EEI/article/view/6863 10.11591/eei.v13i5.6863 |
| spellingShingle | Mohd Khairudin, Nazli Mustapha, Norwati Mohd Aris, Teh Noranis Zolkepli, Maslina Ensemble neural networks with input optimization for flood forecasting |
| title | Ensemble neural networks with input optimization for flood forecasting |
| title_full | Ensemble neural networks with input optimization for flood forecasting |
| title_fullStr | Ensemble neural networks with input optimization for flood forecasting |
| title_full_unstemmed | Ensemble neural networks with input optimization for flood forecasting |
| title_short | Ensemble neural networks with input optimization for flood forecasting |
| title_sort | ensemble neural networks with input optimization for flood forecasting |
| url | http://psasir.upm.edu.my/id/eprint/113840/1/113840.pdf |
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