Kisi, OzgurFedakar, Halil Ibrahim2025-09-252025-09-2520149789401786423978940178641694017864109789401786423https://doi.org/10.1007/978-94-017-8642-3_10https://hdl.handle.net/20.500.12573/4220This chapter proposes fuzzy genetic approach so as to predict suspended sediment concentration (SSC) carried in natural rivers for a given stream cross section. Fuzzy genetic models are improved by combining two methods, fuzzy logic and genetic algorithms. The accuracy of fuzzy genetic models was compared with those of the adaptive network-based fuzzy inference system, multilayer perceptrons, and sediment rating curve models. The daily streamflow and suspended sediment data belonging to two stations, Muddy Creek near Vaughn (Station No: 06088300) and Muddy Creek at Vaughn (Station No: 06088500), operated by the US Geological Survey were used as case studies. The root mean square errors and determination coefficient statistics were used for evaluating the accuracy of the models. The comparison results revealed that the fuzzy genetic approach performed better than the other models in the estimation of the SSC. © 2024 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessAdaptive Network-Based Fuzzy Inference SystemFuzzy Genetic ApproachMultilayer PerceptronsSediment Rating CurveSuspended Sediment ConcentrationFuzzy InferenceFuzzy Neural NetworksFuzzy SystemsGenetic AlgorithmsMean Square ErrorMultilayer Neural NetworksMultilayersSedimentationAdaptive Network-Based Fuzzy Inference SystemAdaptive-Network- Based Fuzzy Inference SystemsFuzzy Genetic ApproachGenetic ApproachGenetic ModelsMultilayers PerceptronsNatural RiverNatural StreamsSediment Rating CurvesSuspended Sediments ConcentrationSuspended SedimentsBook Part10.1007/978-94-017-8642-3_102-s2.0-84931469043