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

Simple item page
dc.citedby3
dc.contributor.authorAbdulmohsin Afan H.en_US
dc.contributor.authorHanna Melini Wan Mohtar W.en_US
dc.contributor.authorAksoy M.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorKhaleel F.en_US
dc.contributor.authorMunir Hayet Khan M.en_US
dc.contributor.authorHatem Kamel A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid58960097300en_US
dc.contributor.authorid58289636100en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57289486500en_US
dc.contributor.authorid16304362800en_US
dc.contributor.authorid57210233114en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:43:05Z
dc.date.available2025-03-03T07:43:05Z
dc.date.issued2024
dc.description.abstractThe 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 AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102760
dc.identifier.doi10.1016/j.asej.2024.102760
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85188914225
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188914225&doi=10.1016%2fj.asej.2024.102760&partnerID=40&md5=aabbd23396df9758105b4bfe112111c7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36561
dc.identifier.volume15
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectArid regions
dc.subjectClimate models
dc.subjectForecasting
dc.subjectMean square error
dc.subjectReservoir management
dc.subjectReservoirs (water)
dc.subjectRivers
dc.subjectSuspended sediments
dc.subjectTropics
dc.subjectAccurate prediction
dc.subjectANN
dc.subjectClimate zone
dc.subjectGA
dc.subjectInput selection
dc.subjectNeural network model
dc.subjectPrediction modelling
dc.subjectSelection based
dc.subjectSuspended sediment loads
dc.subjectTraditional models
dc.subjectNeural network models
dc.titleGeneticizing input selection based advanced neural network model for sediment prediction in different climate zoneen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS