A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran

Abstract

The suspended sediment load estimation of rivers is one of the main issues in hydraulic engineering. Different traditional methods such as sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem of this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were compared to the SRC method. The daily flow discharge and sediment discharge of two hydrometric stations of Kasilian and Telar rivers in the period of 1964–2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R 2 ) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of Kasilian and Telar rivers.