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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River

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dc.citedby1
dc.contributor.authorKatipo?lu O.M.en_US
dc.contributor.authorKartal V.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorid57203751801en_US
dc.contributor.authorid57221197958en_US
dc.contributor.authorid57193547008en_US
dc.date.accessioned2025-03-03T07:41:50Z
dc.date.available2025-03-03T07:41:50Z
dc.date.issued2024
dc.description.abstractThe service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t ? 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = ? 251.090, Bias Factor = ? 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00477-024-02785-1
dc.identifier.epage3927
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85199355668
dc.identifier.spage3907
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199355668&doi=10.1007%2fs00477-024-02785-1&partnerID=40&md5=f0d9592dec2b433465e2d89c26991b95
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36294
dc.identifier.volume38
dc.pagecount20
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleStochastic Environmental Research and Risk Assessment
dc.subjectCoruh River
dc.subjectBioluminescence
dc.subjectBiomimetics
dc.subjectForecasting
dc.subjectNeural networks
dc.subjectReservoir management
dc.subjectReservoirs (water)
dc.subjectRivers
dc.subjectSediments
dc.subjectWater management
dc.subjectArtificial bee colony optimizations
dc.subjectArtificial bees
dc.subjectArtificial neural network algorithm
dc.subjectCoruh river
dc.subjectFirefly algorithms
dc.subjectFirefly optimization
dc.subjectLoad values
dc.subjectOptimisations
dc.subjectOptimization techniques
dc.subjectSediment loads
dc.subjectalgorithm
dc.subjectartificial neural network
dc.subjectbiotechnology
dc.subjectforecasting method
dc.subjectoptimization
dc.subjectsediment transport
dc.subjectOptimization
dc.titleSediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh Riveren_US
dc.typeArticleen_US
dspace.entity.typePublication
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