Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network

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dc.authoridkeblouti, mehdi/0000-0001-6901-4097
dc.authoridAkturk, Gaye/0000-0002-9477-7827
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorAkturk, Gaye
dc.contributor.authorKilinc, Huseyin cagan
dc.contributor.authorTerzioglu, Zeynep ozge
dc.contributor.authorKeblouti, Mehdi
dc.date.accessioned2025-01-21T16:44:47Z
dc.date.available2025-01-21T16:44:47Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractSuspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Ye & scedil;il & imath;rmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973-2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures' effective lifespan and operation and take precautions against sediment-related disaster risks.
dc.identifier.doi10.1007/s12145-024-01338-y
dc.identifier.endpage3649
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85196299648
dc.identifier.scopusqualityQ2
dc.identifier.startpage3623
dc.identifier.urihttps://doi.org/10.1007/s12145-024-01338-y
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25504
dc.identifier.volume17
dc.identifier.wosWOS:001249380200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectMeta-heuristic algorithms; Optimize; Hyperparameter; Prediction; SFLA-ANN; Suspended sediment load
dc.titleSuspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network
dc.typeArticle

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