Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran

Abstract

The present study compares the results of the Soil and Water Assessment Tool (SWAT) with a Support Vector Machine (SVM) to predict the monthly streamflow of arid regions located in the southern part of Iran, namely the Roodan watershed. Data collected over a period of 19 years (1990–2008) was used to predict the monthly streamflow. Calibration (training) and validation (testing) were performed within the same period for both the models after the preparation of the required data. A semi auto-calibration was performed for the SWAT model. Also, the best input combination of the SVM model was identified using the Gamma Test (GT). Finally, the reliability of the SWAT and SVM models were evaluated based on performance criteria such as the Nash-Sutcliffe (NS) model efficiency coefficient and the Root Mean Square Error (RMSE). The obtained results from the development of the SWAT model and SVM model indicated satisfying performance in predicting the monthly streamflow in the large arid region. The SWAT obtained NS and RMSE values of 0.83 and 6.1 respectively, and the SVM obtained NS and RMSE values of 0.84 and 6.75 respectively for the validation (testing) period. Results indicate that for high flows of more than 19 (m3/s), both models predict flow with over and under estimation in the validation (testing) period. Moreover, the SVM has a closer value for the average flow in comparison to the SWAT model; whereas the SWAT model outperformed for total runoff volume with a lower error in the validation period.