Simulation of monthly river flow using improved Support Vector Machine Regression (SVR) model using Gray Wolf Optimization (GWO) algorithm.

Articles in Press

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Abstract

The aim of this study is to improve the performance of the Support Vector Regression(SVR) model, using the Gray Wolf Optimization(GWO) algorithm for monthly river flow modeling. For this purpose, monthly data of 15 years(from 1400 to 1385) of river flow, rainfall and temperature were used. The trial and error method was 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) and T(t-1) are the best independent variables for simulating the Qt variable. 80% of the data was used for training and 20% of the data was used for validating the SVR and GWO-SVR models. R^2, RMS and NSE indices were also used to evaluate the efficiency of the models. Also, linear activation functions(LKF), polynomial(PKF), radial basis function(RBF), and sigmoid(SKF) were used to develop the models. The trial and error method was used to determine the parameters of the activator functions. Based on the results of this study, the SVR model with polynomial activation function has the best performance in the training and validation phase, and with the linear activation function, it has the worst performance in the training and validation phase. Next, the GWO algorithm was used to determine the parameters of the activator functions. Based on the final results, the SVR model performs better with the GWO algorithm. Therefore, to simulate the monthly flow of river water using this model, it is better to use the GWO algorithm instead of the trial and error method.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 26 November 2024

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