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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...

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Main Authors: Mohd Khairudin, Nazli, Mustapha, Norwati, Mohd Aris, Teh Noranis, Zolkepli, Maslina
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113840/1/113840.pdf
http://psasir.upm.edu.my/id/eprint/113840/
https://beei.org/index.php/EEI/article/view/6863
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spelling my.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Mohd Khairudin, Nazli
Mustapha, Norwati
Mohd Aris, Teh Noranis
Zolkepli, Maslina
spellingShingle Mohd Khairudin, Nazli
Mustapha, Norwati
Mohd Aris, Teh Noranis
Zolkepli, Maslina
Ensemble neural networks with input optimization for flood forecasting
author_facet Mohd Khairudin, Nazli
Mustapha, Norwati
Mohd Aris, Teh Noranis
Zolkepli, Maslina
author_sort Mohd Khairudin, Nazli
title Ensemble neural networks with input optimization for flood forecasting
title_short 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_sort ensemble neural networks with input optimization for flood forecasting
publisher Institute of Advanced Engineering and Science
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113840/1/113840.pdf
http://psasir.upm.edu.my/id/eprint/113840/
https://beei.org/index.php/EEI/article/view/6863
_version_ 1823093076784054272
score 13.239859

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