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Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

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dc.citedby51
dc.contributor.authorBanadkooki F.B.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorTeo F.Y.en_US
dc.contributor.authorEbrahimi M.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57201068611en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid35249518400en_US
dc.contributor.authorid57209555582en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:07:24Z
dc.date.available2023-05-29T08:07:24Z
dc.date.issued2020
dc.descriptionalgorithm; Iran; uncertainty; Algorithms; Iran; Neural Networks, Computer; Uncertaintyen_US
dc.description.abstractSuspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-020-09876-w
dc.identifier.epage38116
dc.identifier.issue30
dc.identifier.scopus2-s2.0-85087512372
dc.identifier.spage38094
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087512372&doi=10.1007%2fs11356-020-09876-w&partnerID=40&md5=6f07c348d169959f69bc8e4134f7bc5c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25218
dc.identifier.volume27
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleSuspended sediment load prediction using artificial neural network and ant lion optimization algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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