Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches

Prediction of sediment concentration in a river is very important for many water resource projects. Conventional sediment rating curves (SRC), however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. A comparison was performed between fuzzy logic (FL), SRC and multilinear regression models. It was based on a 5-year period of continuous streamflow, suspended sediment concentration and mean water temperature data of Sacremento Freeport Station operated by the United States Geological Survey. Based on the comparison of the results, it is found that the FL model gives better estimates than the other techniques.