Publication:
Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions

Simple item page
dc.citedby27
dc.contributor.authorEhteram M.en_US
dc.contributor.authorGhotbi S.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorKrishnan M.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorEL-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57210611785en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57211283663en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:23:28Z
dc.date.available2023-05-29T07:23:28Z
dc.date.issued2019
dc.description.abstractSuspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4149
dc.identifier.doi10.3390/app9194149
dc.identifier.issue19
dc.identifier.scopus2-s2.0-85073276948
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073276948&doi=10.3390%2fapp9194149&partnerID=40&md5=b51fa04818b4899a77446ecb1b67a4db
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24431
dc.identifier.volume9
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleInvestigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictionsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS