Staff View: Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm

Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm

The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network�whale algorithm (WA) model, the multilayer perception (MLP�WA) model, and genetic programming (GP) were us...

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Main Authors:	Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Fai C.M., Afan H.A., Sapitang M., El-Shafie A.
Other Authors:	57201068611
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Published:	Springer 2023
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spelling	my.uniten.dspace-252272023-05-29T16:07:26Z Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Fai C.M. Afan H.A. Sapitang M. El-Shafie A. 57201068611 57113510800 57214837520 35249518400 57214146115 56436626600 57215211508 16068189400 The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network�whale algorithm (WA) model, the multilayer perception (MLP�WA) model, and genetic programming (GP) were used to predict GWL. The objectives were: (1) to prepare robust hybrid ANN models; (2) to study the combination of ANN models and optimization algorithms; and (3) to study uncertainty related to the input parameters of the models, whereby three scenarios with different inputs were considered. The results showed that for the first scenario, in which the input data were just the average of the region temperature and three temporal delays of 3, 6, and 9�months were considered, the models based on the three simultaneous temperature inputs with mentioned delays had higher performance as compared to the inputs just belonging to temperature input. The MLP�WA model was the best model among all. For the test stage, the mean absolute error of the MLP�WA model decreased to 30% and from 31 to 38% as compared to the radial basis function�whale algorithm (RBF�WA) and GP models, respectively. The second scenario was the evaluation of the predicted GWL based on the precipitation data of 3, 6, and 9�months. The results showed that the three variations of precipitation data as simultaneous input improved the models� performance. The third scenario was considered in which the data from average precipitation and temperature were simultaneously used. The best results were obtained when the precipitation and temperature data with delays of 3, 6, and 9�months were used as input. � 2020, International Association for Mathematical Geosciences. Final 2023-05-29T08:07:26Z 2023-05-29T08:07:26Z 2020 Article 10.1007/s11053-020-09634-2 2-s2.0-85080148753 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080148753&doi=10.1007%2fs11053-020-09634-2&partnerID=40&md5=b669b6c727c745e6099ed387167cfda8 https://irepository.uniten.edu.my/handle/123456789/25227 29 5 3233 3252 Springer 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/
description	The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network�whale algorithm (WA) model, the multilayer perception (MLP�WA) model, and genetic programming (GP) were used to predict GWL. The objectives were: (1) to prepare robust hybrid ANN models; (2) to study the combination of ANN models and optimization algorithms; and (3) to study uncertainty related to the input parameters of the models, whereby three scenarios with different inputs were considered. The results showed that for the first scenario, in which the input data were just the average of the region temperature and three temporal delays of 3, 6, and 9�months were considered, the models based on the three simultaneous temperature inputs with mentioned delays had higher performance as compared to the inputs just belonging to temperature input. The MLP�WA model was the best model among all. For the test stage, the mean absolute error of the MLP�WA model decreased to 30% and from 31 to 38% as compared to the radial basis function�whale algorithm (RBF�WA) and GP models, respectively. The second scenario was the evaluation of the predicted GWL based on the precipitation data of 3, 6, and 9�months. The results showed that the three variations of precipitation data as simultaneous input improved the models� performance. The third scenario was considered in which the data from average precipitation and temperature were simultaneously used. The best results were obtained when the precipitation and temperature data with delays of 3, 6, and 9�months were used as input. � 2020, International Association for Mathematical Geosciences.
author2	57201068611
author_facet	57201068611 Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Fai C.M. Afan H.A. Sapitang M. El-Shafie A.
format	Article
author	Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Fai C.M. Afan H.A. Sapitang M. El-Shafie A.
spellingShingle	Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Fai C.M. Afan H.A. Sapitang M. El-Shafie A. Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
author_sort	Banadkooki F.B.
title	Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
title_short	Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
title_full	Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
title_fullStr	Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
title_full_unstemmed	Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
title_sort	enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm
publisher	Springer
publishDate	2023
_version_	1806424369735401472
score	13.222552

Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm

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