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 W.J., Chong K.L., Ahmed A.N., Malek M.B.A., Huang Y.F., Sherif M., Elshafie A.
Other Authors: 57226181151
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Published: Springer Science and Business Media Deutschland GmbH 2024
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spelling my.uniten.dspace-346712024-10-14T11:21:36Z Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia Wee W.J. Chong K.L. Ahmed A.N. Malek M.B.A. Huang Y.F. Sherif M. Elshafie A. 57226181151 57208482172 57214837520 55636320055 55807263900 7005414714 16068189400 Artificial neural network Bat meta-heuristic algorithm Streamflow forecasting Uncertainty analysis Malaysia Flood control Forecasting Heuristic algorithms Heuristic methods Neural networks Stream flow Artificial neural network modeling Bat algorithms Bat meta-heuristic algorithm Flood waters Malaysia Meta-heuristics algorithms Performance River inflow Streamflow forecasting Study areas algorithm artificial neural network forecasting method inflow river flow streamflow uncertainty analysis Uncertainty analysis 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�m3/s for training and 0.143�m3/s for testing as compared to ANN standalone training and testing yielding 0.091�m3/s and 0.116�m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 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. � 2022, The Author(s). Final 2024-10-14T03:21:36Z 2024-10-14T03:21:36Z 2023 Article 10.1007/s13201-022-01831-z 2-s2.0-85144222517 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144222517&doi=10.1007%2fs13201-022-01831-z&partnerID=40&md5=bca1d467a317e3a958d7bf4acd2d76c6 https://irepository.uniten.edu.my/handle/123456789/34671 13 1 30 All Open Access Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Artificial neural network
Bat meta-heuristic algorithm
Streamflow forecasting
Uncertainty analysis
Malaysia
Flood control
Forecasting
Heuristic algorithms
Heuristic methods
Neural networks
Stream flow
Artificial neural network modeling
Bat algorithms
Bat meta-heuristic algorithm
Flood waters
Malaysia
Meta-heuristics algorithms
Performance
River inflow
Streamflow forecasting
Study areas
algorithm
artificial neural network
forecasting method
inflow
river flow
streamflow
uncertainty analysis
Uncertainty analysis
spellingShingle Artificial neural network
Bat meta-heuristic algorithm
Streamflow forecasting
Uncertainty analysis
Malaysia
Flood control
Forecasting
Heuristic algorithms
Heuristic methods
Neural networks
Stream flow
Artificial neural network modeling
Bat algorithms
Bat meta-heuristic algorithm
Flood waters
Malaysia
Meta-heuristics algorithms
Performance
River inflow
Streamflow forecasting
Study areas
algorithm
artificial neural network
forecasting method
inflow
river flow
streamflow
uncertainty analysis
Uncertainty analysis
Wee W.J.
Chong K.L.
Ahmed A.N.
Malek M.B.A.
Huang Y.F.
Sherif M.
Elshafie A.
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�m3/s for training and 0.143�m3/s for testing as compared to ANN standalone training and testing yielding 0.091�m3/s and 0.116�m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 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. � 2022, The Author(s).
author2 57226181151
author_facet 57226181151
Wee W.J.
Chong K.L.
Ahmed A.N.
Malek M.B.A.
Huang Y.F.
Sherif M.
Elshafie A.
format Article
author Wee W.J.
Chong K.L.
Ahmed A.N.
Malek M.B.A.
Huang Y.F.
Sherif M.
Elshafie A.
author_sort Wee W.J.
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 Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061189961875456
score 13.209306