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

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dc.authorid/0000-0003-1738-3565
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorKartal, Veysi
dc.contributor.authorPande, Chaitanya Baliram
dc.date.accessioned2024-12-24T19:24:25Z
dc.date.available2024-12-24T19:24:25Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
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 & Ccedil;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.
dc.description.sponsorshipErzincan Binali Yildirim University; General Directorate of State Hydraulic Works
dc.description.sponsorshipThe author would like to express gratitude to the General Directorate of State Hydraulic Works for providing the data. Thanks to Grammarly Premium Software, which helps correct grammatical errors and spelling mistakes in article. Thanks are extended to the Editor and the anonymous reviewers for their valuable contributions to the content and development of this paper.
dc.identifier.doi10.1007/s00477-024-02785-1
dc.identifier.endpage3927
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85199355668
dc.identifier.scopusqualityQ1
dc.identifier.startpage3907
dc.identifier.urihttps://doi.org/10.1007/s00477-024-02785-1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5985
dc.identifier.volume38
dc.identifier.wosWOS:001274784800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofStochastic Environmental Research and Risk Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectSediment load
dc.subjectANN
dc.subjectFirefly optimization
dc.subjectArtificial bee colony optimization
dc.subject& Ccedil;oruh river
dc.titleSediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River
dc.typeArticle

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