Comparison of the Efficiency of Support Vector Regression and K-Nearest Neighbor Methods in suspended sediment load Estimation in river (Case Study: Lighvan Chay River)

Estimation of suspended sediment load is one of the most important and fundamental challenges in the studies of sediment transport and river engineering, due to the damage caused by the lack of attention and considering it. Given the importance and role of sediment in the design and maintenance of hydraulic structures such as dams and As well as planning for efficient use of downstream of river and also conservation of nutrients at the upstream of river always lots of efforts have been done in the field of suspended sediment load estimation and numerical methods have been developed in this case. But due to the cost of most procedures or lack of adequate accuracy in most of common experimental methods, need to a new method that can estimate suspended sediment load with the greatest possible precision, seems to be very necessary. In this study the amounts of suspended sediment loads have been estimated with support vector regression and k-Nearest neighbor methods. Results indicated the acceptable ability of both data mining techniques that explored in this study in estimation of suspended sediment load. Among the methods examined in this study, the support vector regression method estimated the amounts of suspended sediment load in Lighvan Chay River with representing evaluation indexes such as (CC=0.959, RMSE=43.547(ton/day)) is more accurate rather than K-nearest neighbor method.