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Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and...

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Main Authors:	Abdulmohsin Afan H., Hanna Melini Wan Mohtar W., Aksoy M., Najah Ahmed A., Khaleel F., Munir Hayet Khan M., Hatem Kamel A., Sherif M., El-Shafie A.
Other Authors:	56436626600
Format:	Article
Published:	Ain Shams University 2025
Subjects:	Arid regions Climate models Forecasting Mean square error Reservoir management Reservoirs (water) Rivers Suspended sediments Tropics Accurate prediction ANN Climate zone GA Input selection Neural network model Prediction modelling Selection based Suspended sediment loads Traditional models Neural network models
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id	my.uniten.dspace-36561
record_format	dspace
spelling	my.uniten.dspace-365612025-03-03T15:43:05Z Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone Abdulmohsin Afan H. Hanna Melini Wan Mohtar W. Aksoy M. Najah Ahmed A. Khaleel F. Munir Hayet Khan M. Hatem Kamel A. Sherif M. El-Shafie A. 56436626600 58960097300 58289636100 57214837520 57289486500 16304362800 57210233114 7005414714 16068189400 Arid regions Climate models Forecasting Mean square error Reservoir management Reservoirs (water) Rivers Suspended sediments Tropics Accurate prediction ANN Climate zone GA Input selection Neural network model Prediction modelling Selection based Suspended sediment loads Traditional models Neural network models The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation. ? 2024 THE AUTHORS Final 2025-03-03T07:43:05Z 2025-03-03T07:43:05Z 2024 Article 10.1016/j.asej.2024.102760 2-s2.0-85188914225 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188914225&doi=10.1016%2fj.asej.2024.102760&partnerID=40&md5=aabbd23396df9758105b4bfe112111c7 https://irepository.uniten.edu.my/handle/123456789/36561 15 7 102760 All Open Access; Gold Open Access Ain Shams University 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	Arid regions Climate models Forecasting Mean square error Reservoir management Reservoirs (water) Rivers Suspended sediments Tropics Accurate prediction ANN Climate zone GA Input selection Neural network model Prediction modelling Selection based Suspended sediment loads Traditional models Neural network models
spellingShingle	Arid regions Climate models Forecasting Mean square error Reservoir management Reservoirs (water) Rivers Suspended sediments Tropics Accurate prediction ANN Climate zone GA Input selection Neural network model Prediction modelling Selection based Suspended sediment loads Traditional models Neural network models Abdulmohsin Afan H. Hanna Melini Wan Mohtar W. Aksoy M. Najah Ahmed A. Khaleel F. Munir Hayet Khan M. Hatem Kamel A. Sherif M. El-Shafie A. Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
description	The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation. ? 2024 THE AUTHORS
author2	56436626600
author_facet	56436626600 Abdulmohsin Afan H. Hanna Melini Wan Mohtar W. Aksoy M. Najah Ahmed A. Khaleel F. Munir Hayet Khan M. Hatem Kamel A. Sherif M. El-Shafie A.
format	Article
author	Abdulmohsin Afan H. Hanna Melini Wan Mohtar W. Aksoy M. Najah Ahmed A. Khaleel F. Munir Hayet Khan M. Hatem Kamel A. Sherif M. El-Shafie A.
author_sort	Abdulmohsin Afan H.
title	Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
title_short	Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
title_full	Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
title_fullStr	Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
title_full_unstemmed	Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
title_sort	geneticizing input selection based advanced neural network model for sediment prediction in different climate zone
publisher	Ain Shams University
publishDate	2025
_version_	1825816026709753856
score	13.244413

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

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