Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach

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dc.contributor.author Kisi, Ozgur
dc.contributor.author Fedakar, Halil Ibrahim
dc.date.accessioned 2025-09-25T10:50:56Z
dc.date.available 2025-09-25T10:50:56Z
dc.date.issued 2014
dc.description.abstract This 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. en_US
dc.identifier.doi 10.1007/978-94-017-8642-3_10
dc.identifier.isbn 9789401786423
dc.identifier.isbn 9789401786416
dc.identifier.isbn 9401786410
dc.identifier.isbn 9789401786423
dc.identifier.scopus 2-s2.0-84931469043
dc.identifier.uri https://doi.org/10.1007/978-94-017-8642-3_10
dc.identifier.uri https://hdl.handle.net/20.500.12573/4220
dc.language.iso en en_US
dc.publisher Springer Netherlands en_US
dc.relation.ispartof Computational Intelligence Techniques in Earth and Environmental Sciences
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Adaptive Network-Based Fuzzy Inference System en_US
dc.subject Fuzzy Genetic Approach en_US
dc.subject Multilayer Perceptrons en_US
dc.subject Sediment Rating Curve en_US
dc.subject Suspended Sediment Concentration en_US
dc.subject Fuzzy Inference en_US
dc.subject Fuzzy Neural Networks en_US
dc.subject Fuzzy Systems en_US
dc.subject Genetic Algorithms en_US
dc.subject Mean Square Error en_US
dc.subject Multilayer Neural Networks en_US
dc.subject Multilayers en_US
dc.subject Sedimentation en_US
dc.subject Adaptive Network-Based Fuzzy Inference System en_US
dc.subject Adaptive-Network- Based Fuzzy Inference Systems en_US
dc.subject Fuzzy Genetic Approach en_US
dc.subject Genetic Approach en_US
dc.subject Genetic Models en_US
dc.subject Multilayers Perceptrons en_US
dc.subject Natural River en_US
dc.subject Natural Streams en_US
dc.subject Sediment Rating Curves en_US
dc.subject Suspended Sediments Concentration en_US
dc.subject Suspended Sediments en_US
dc.title Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach en_US
dc.type Book Part en_US
dspace.entity.type Publication
gdc.author.scopusid 6507051085
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gdc.coar.access metadata only access
gdc.coar.type text::book::book part
gdc.collaboration.industrial false
gdc.description.department Abdullah Gül University en_US
gdc.description.departmenttemp [Kisi] Ozgur, Department of Civil Engineering, Canik Ba͆ar University, Samsun, Turkey; [Fedakar] Halil Ibrahim, Department of Civil Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey en_US
gdc.description.endpage 196 en_US
gdc.description.publicationcategory Kitap Bölümü - Uluslararası en_US
gdc.description.scopusquality N/A
gdc.description.startpage 175 en_US
gdc.description.volume 9789401786423
gdc.description.wosquality N/A
gdc.identifier.openalex W1886225782
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gdc.openalex.fwci 1.46
gdc.openalex.normalizedpercentile 0.84
gdc.opencitations.count 2
gdc.plumx.crossrefcites 2
gdc.plumx.mendeley 5
gdc.plumx.scopuscites 6
gdc.scopus.citedcount 6
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