Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm

SummaryEstimation of suspended sediment concentration carried by a river is very important for many water resources projects. The accuracy of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm is investigated in this paper for modeling discharge-suspended sediment relationship. The ANN–ABC was compared with those of the neural differential evolution, adaptive neuro-fuzzy, neural networks and rating curve models. The daily stream flow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, were used as case studies. For evaluating the ability of the models, mean square error and determination coefficient criteria were used. Comparison results showed that the ANN–ABC was able to produce better results than the neural differential evolution, neuro-fuzzy, neural networks and rating curve models. The logarithm transformed data were also used as input to the proposed ANN–ABC models. It was found that the logarithm transform significantly increased accuracy of the models in suspended sediment estimation.

► We use ANN with artificial bee colony (ANN–ABC) algorithm to model sediment load.
► The ANN–ABC is compared with other soft computational methods and rating curve models.
► These methods are neural differential evolution, adaptive neuro-fuzzy and ANN.
► Comparison results indicate that the ANN–ABC models performed better than the others.
► The logarithm transform is also found to significantly increase models’ accuracy.