Simulation of monthly river flow using improved support vector machine regression model using gray wolf optimization algorithm

Measuring the flow of rivers is one of the most important issues in river management, that's why it is always tried to use accurate methods for its measurement. The aim of this study is to enhance the performance of Support Vector Regression (SVR) model using the Gray Wolf Optimization (GWO) algorithm for monthly river flow modeling. For this purpose, the monthly data of river flow, precipitation and temperature during 15 years (from 1400 to 2015) are used. The trial and error procedure is used to select the best input variables to the SVR and GWO-SVR models. Based on the results of this method, Q(t-1), R(t-1), T(t-1) are the best independent variables for simulating the variable Q_t. 80% of all data are used for training and 20% for validating the SVR and GWO-SVR models. Also, R^2, RMS and NSE indices are utilized to evaluate the efficiency of the models, linear (LKF), polynomial (PKF), radial basis function (RBF), and sigmoid (SKF) activation functions are used to develop the models. First, the trial and error procedure is used to determine the parameters of the activation functions. Based on the results of this study, the SVR model with the polynomial activation function has the best performance in the training and validation stage, and the worst performance with the linear activation function in the training and verification stages. Then, the GWO algorithm is used to determine the parameters of the activation functions. Based on the results, the SVR model performs better with the GWO algorithm. Therefore, to simulate the monthly flow of river using this model, it is better to use the GWO algorithm instead of the trial and error procedure.