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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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 |
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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 |
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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 |
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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). |
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57226181151 |
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57226181151 Wee W.J. Chong K.L. Ahmed A.N. Malek M.B.A. Huang Y.F. Sherif M. Elshafie A. |
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Article |
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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 |
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Springer Science and Business Media Deutschland GmbH |
publishDate |
2024 |
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13.209306 |