Performance Analysis of Hydrological and Data Based models in Estimation of Suspended Sediment Rate

Data driven models are proposed as an alternative to hydrological methods in sediment estimation calculations. The aim of this study was to compare the performance and accuracy of hydrological and data-based methods in estimating the amount of suspended sediment. For this purpose, discharge and sediment data were collected in the period of 20 yr (2001-2011) and then the amount of suspended sediment of Bagh Kalayeh hydrometric station on Alamut River in Qazvin province was estimated. In this study hydrological methods including Smearing, FAO and Sediment Rating Curves versus data driven methods including Gene Expression Programming, Instance-Based Learning with parameter K and Linear Regression methods were used. The model performances were compared using two statistical methods of RRMSE and NS. The results showed that two techniques such as IBK model with evaluation criteria of (R = 0.94, RRMSE = 0.29 and NS = 0.24) and the GEP model with (R = 0.85, RRMSE = 0.59 and NS = 0.65) estimated suspended sediment in more accurate way than other studies methods. Thus, the superiority of data-driven methods in estimating the amount of suspended sediment in the study area was proved. Therefore, the use of data-based techniques as a competitor and alternative to hydrological methods to estimate the amount of suspended sediment in areas similar to the study area is recommended.