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Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia

Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The ba...

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Main Authors:	Wee, Wei Joe, Chong, Kai Lun, Ahmed, Ali Najah, Malek, Marlinda Binti Abdul, Huang, Yuk Feng, Sherif, Mohsen, Elshafie, Ahmed
Format:	Article
Published:	Springer Heidelberg 2023
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/39129/
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id	my.um.eprints.39129
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spelling	my.um.eprints.391292023-07-05T07:17:05Z http://eprints.um.edu.my/39129/ Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia Wee, Wei Joe Chong, Kai Lun Ahmed, Ali Najah Malek, Marlinda Binti Abdul Huang, Yuk Feng Sherif, Mohsen Elshafie, Ahmed TA Engineering (General). Civil engineering (General) Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m(3)/s for training and 0.143 m(3)/s for testing as compared to ANN standalone training and testing yielding 0.091 m(3)/s and 0.116 m(3)/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R-2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R-2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work's performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case. Springer Heidelberg 2023-01 Article PeerReviewed Wee, Wei Joe and Chong, Kai Lun and Ahmed, Ali Najah and Malek, Marlinda Binti Abdul and Huang, Yuk Feng and Sherif, Mohsen and Elshafie, Ahmed (2023) Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia. Applied Water Science, 13 (1). ISSN 2190-5487, DOI https://doi.org/10.1007/s13201-022-01831-z <https://doi.org/10.1007/s13201-022-01831-z>. 10.1007/s13201-022-01831-z
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Wee, Wei Joe Chong, Kai Lun Ahmed, Ali Najah Malek, Marlinda Binti Abdul Huang, Yuk Feng Sherif, Mohsen Elshafie, Ahmed Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
description	Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m(3)/s for training and 0.143 m(3)/s for testing as compared to ANN standalone training and testing yielding 0.091 m(3)/s and 0.116 m(3)/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R-2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R-2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work's performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case.
format	Article
author	Wee, Wei Joe Chong, Kai Lun Ahmed, Ali Najah Malek, Marlinda Binti Abdul Huang, Yuk Feng Sherif, Mohsen Elshafie, Ahmed
author_facet	Wee, Wei Joe Chong, Kai Lun Ahmed, Ali Najah Malek, Marlinda Binti Abdul Huang, Yuk Feng Sherif, Mohsen Elshafie, Ahmed
author_sort	Wee, Wei Joe
title	Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
title_short	Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
title_full	Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
title_fullStr	Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
title_full_unstemmed	Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
title_sort	application of augmented bat algorithm with artificial neural network in forecasting river inflow in malaysia
publisher	Springer Heidelberg
publishDate	2023
url	http://eprints.um.edu.my/39129/
_version_	1772811757148962816
score	13.211869

Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia

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