Comparison of Artificial Neural Networks, Adaptive Neuro-Fuzzy and Sediment Rating Curve Models for Estimating Suspended Sediment Load of Ajichay River

In water construction projects, river engineering, and irrigation and drainage engineering, it is vital to estimate the accurate volume of the sediment transported by rivers. As the sediment transport phenomenon is an immensely complex problem, therefore presenting an appropriate solution for precise evaluation of the suspended load in rivers is tedious and the mathematical models are not also accurate enough to be applied. Nowadays application of artificial intelligence systems has been developed as a novel solution in analysis of water resources problems. In this research, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Artificial Neural Networks (ANNs) models were utilized to determine suspended sediment rate of Ajichay River. Discharge, sediment load and water level data were used to prepare the models and obtain sediment rating curves. The statistical period is also divided into three seasons, namely dry, wet and snow melting. The accuracy of the models for these periods has been tested. The results showed that ANFIS neuro-fuzzy had better accuracy for determination of suspend sediment loads in comparison with both the ANNS and the rating curve.