Comparison and Evaluation of Support Vector Machine and Gene Programming in River Suspended Sediment Estimation (Case Study: Kashkan River)

Abstract

Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.