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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2016-213
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
31 May 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). A final paper in HESS is not foreseen.
A new approach for modeling suspended sediment: Evolutionary fuzzy approach
Ozgur Kisi Civil Engineering Department, Faculty of Engineering and Architecture, Canik Basari University, Samsun, Turkey
Abstract. This paper proposes the application of evolutionary fuzzy (EF) approach for prediction of daily suspended sediment concentration (SSC). The EF was improved by the combination of two methods, fuzzy logic and genetic algorithm. The accuracy of EF models is compared with those of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM). The daily streamflow and suspended sediment data collected from two stations on the Eel River in California, United States are used in the study. Root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient criteria are used for evaluating the accuracy of the models. The EF is found to be superior to the ANN and ANFIS-FCM in SSC prediction. The relative RMSE and MAE differences between the optimal EF and ANN models were found to be 13–50 % and 15–65 % for the upstream and downstream stations, respectively. Comparison of the optimal EF, ANN and ANFIS-FCM models in estimating peak and total suspended sediments revealed that the EF model provided better accuracy than the ANN and ANFIS-FCM.

Citation: Kisi, O.: A new approach for modeling suspended sediment: Evolutionary fuzzy approach, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-213, 2016.
Ozgur Kisi
Ozgur Kisi

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Short summary
Evolutionary fuzzy (EF) approach is used for predicting suspended sediment concentration (SSC). The accuracy of EF models is compared with neuro-fuzzy (ANFIS) and neural network (NN) models. The EF is found to be superior to the ANFIS and NN in SSC prediction. The relative RMSE and MAE differences between the optimal EF and ANN models are found to be 13–50 % and 15–65 % for the upstream and downstream stations, respectively.
Evolutionary fuzzy (EF) approach is used for predicting suspended sediment concentration (SSC)....
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