University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 52 5 2021 07 23 Rainfall- Runoff Modeling Using HBV Model and Random Forest Algorithm in Bazoft Watershed Rainfall- Runoff Modeling Using HBV Model and Random Forest Algorithm in Bazoft Watershed 1395 1407 82453 10.22059/ijswr.2021.318980.668893 FA Fatemeh Sohrabi Geshnigani MSc Student of Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran. Rasoul Mirabbasi Najafabadi Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran. 0000-0002-9897-0042 Mohammad Reza Golabi Ph.D of Hydrology and Water Resources, Department of Water Engineering, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IRAN. Journal Article 2021 02 12 Estimation of runoff in a catchment area is important from various aspects such as dam reservoir management, water resources management, flood regulation, and erosion control in river banks and bed. In the present study, a conceptual model of HBV and an intelligent model of Random Forest (RF) were used to simulate the rainfall- runoff process in Bazoft watershed at the Landi hydrometric station during the period of 2010 to 2017. In order to evaluate the performance of models, the statistical criteria, including Correlation coefficient (r), Root Mean Squares Error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) were used. Comparing the results of HBV and RF models revealed that the RF model outperformed the HBV. Thus, the RF model with r=0.95, NS=0.82, MAPE=9.59, MAE=0.25, and RMSE=0.39 m3/s was selected as the top model which might be used as a new choice to predict runoff in Bazoft watershed. Estimation of runoff in a catchment area is important from various aspects such as dam reservoir management, water resources management, flood regulation, and erosion control in river banks and bed. In the present study, a conceptual model of HBV and an intelligent model of Random Forest (RF) were used to simulate the rainfall- runoff process in Bazoft watershed at the Landi hydrometric station during the period of 2010 to 2017. In order to evaluate the performance of models, the statistical criteria, including Correlation coefficient (r), Root Mean Squares Error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) were used. Comparing the results of HBV and RF models revealed that the RF model outperformed the HBV. Thus, the RF model with r=0.95, NS=0.82, MAPE=9.59, MAE=0.25, and RMSE=0.39 m3/s was selected as the top model which might be used as a new choice to predict runoff in Bazoft watershed. https://ijswr.ut.ac.ir/article_82453_65f07291946d77c52ae1ebe57532ba52.pdf