『Abstract
　This paper proposes the application of evolutionary fuzzy models (EFMs) for suspended sediment concentration estimation. The EFMs are improved by the combination of two methods, fuzzy logic and differential evolution. The accuracy of EFMs is compared with those of the adaptive neuro-fuzzy, neutral networks and rating curve models. The daily streamflow and suspended sediment data belonging to two stations, Quebrada Blanca Station and Rio Valenciano Station, operated by the US Geological Survey (USGS) are used as case studies. The mean square errors and determination coefficient statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the EFMs give better estimates than the other techniques

Keywords: Suspended sediment; Fuzzy Modelling; Differential evolution; Neuro-fuzzy; Neural networks; Rating curve』

Introduction
Methodology
　Fuzzy logic approach
　Differential evolution (DE)
Case study
Application and results
Conclusions
Acknowledgements
References