Evolutionary fuzzy models for river suspended sediment concentration estimation

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, neural 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.