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

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.

Keywords

  • Suspended sediment concentration
  • Fuzzy genetic approach
  • Adaptive network-based fuzzy inference system
  • Multilayer perceptrons
  • Sediment rating curve

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Acknowledgements

The data used in this study were downloaded from the web server of the USGS. The authors wish to thank the staff of the USGS who are associated with data observation, processing, and management of USGS web sites.

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Correspondence to Özgür Kişi .

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Kişi, Ö., Fedakar, H.İ. (2014). Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach. In: Islam, T., Srivastava, P., Gupta, M., Zhu, X., Mukherjee, S. (eds) Computational Intelligence Techniques in Earth and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8642-3_10

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