Application of Support Vector Machine for River flow Estimation

Abstract

In recent years application of intelligent methods has been considered in forecasting hydrologic processes. In this research, month river discharge of kakareza, a river located in lorestan province at the west of Iran, was forecasted using Support vector machine and as genetic programming Inference System methods in dehno stations. In this regard, some different combinations in the period (1979-2015) as input data for estimation of discharge in the month index were evaluated. Criteria of correlation coefficient, root mean square error and Nash Sutcliff coefficient to evaluate and compare the performance of methods were used. It showed that combined structure by using surveyed inelegant methods, resulted to an acceptable estimation of discharge to the kakareza river. In addition comparison between models shows that Support vector machine has a better performance than other models in inflow estimation. In terms of accuracy, Support vector machine with correlation coefficients ( 0.970 ) has more propriety than root mean square error (0.08m3 /s ) and Nash Sutcliff ( 0.94 ) . To sum up, it is mentioned that Support vector machine method has a better capability to estimate the minimum, maximum and other flow values.