Application of the Intelligent Systems and Statistical Models in Estimation of Suspended Sediment Distribution

Understanding the vertical distribution of suspended sediment in channels and natural waterways is very important in estimation of the suspended load. High costs، time-consuming of the sampling operations from rivers، and remarkable error associated with equipment and sampling methods، have led engineers and researchers to perform the simulation models and the new statistical methods. In this study، three methods namely artificial neural networks (ANN)، adaptive neuro-fuzzy inference system (ANFIS)، and multivariate linear regression were used to estimate the distribution of suspended sediment concentration. The accuracy of each method was assessed using the most reliable experimental data. After evaluating the performance of the three aforesaid methods، it was found that the ANN method with the values of 0. 999 and 0. 042 for r and RMSE، respectively، had a relative advantage as compared with the other methods. This method precisely estimated the distribution of sediment concentration. Also the adaptive neuro-fuzzy inference system accuracy، with the values of 0. 994 and 0. 042 for r and RMSE، respectively، ranked in the second position. Two equations were proposed for the currents on the smooth and rough beds، using the multivariate regression approach. The results showed that the multivariate regression model had less efficiency than the two aforementioned models. By comparing these models with existing empirical equations such as Rouse''s equation and the equation of Einstein and Chien، it was revealed that statistical methods had estimated the distribution of sediment concentration more accurately than empirical equations.