Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach
| 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.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 | |
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| 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 |
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| gdc.virtual.author | Fedakar, Halil İbrahim | |
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