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Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia by Wei Joe Wee; Kai Lun Chong; Ali Najah Ahmed; Marlinda Binti Abdul Malek; Yuk Feng Huang; Mohsen Sherif; Ahmed Elshafie is a Environmental Science article available to read on EtoBox.

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## Abstract 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

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Author

Wei Joe Wee; Kai Lun Chong; Ali Najah Ahmed; Marlinda Binti Abdul Malek; Yuk Feng Huang; Mohsen Sherif; Ahmed Elshafie

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)