Rainfall- Runoff Modeling Using HBV Model and Random Forest Algorithm in Bazoft Watershed

Document Type : Research Paper


1 MSc Student of Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

3 Ph.D of Hydrology and Water Resources, Department of Water Engineering, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IRAN.


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.


Adnan, R.M., Yuan, X., Kisi, O., Adnan, F., and Mehmood, A. (2018). Stream flow forecasting of poorly gauged mountainous watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree using climatic data from Nearby Station. Water Resources Management, 32: 4469- 4486.
Ahmadi, M., Dadashi Roudbari, A., and Deyrmajai, A. (2020). Runoff estimation using IHACRES model based on CHIRPS satellite data and CMIP5 models (Case study: Gorganroud Basin- Aq Qala Area. Iranian Journal of Soil and Water Research, 51(3): 659-671. (In Farsi)
Ahmadpour, A., Mirhashemi, S., and Haghighatjou, P. (2020). Evaluation of classical, conceptual IHACRES and hybrid ARMA-ANN models in simulation and prediction of daily discharge of Maroun River. Iranian Journal of Soil and Water Research, 51(3): 727-736. ( In Farsi)
Aronica, G.T. and Candela, A. (2007). Derivation of flood frequency curves in poorly gauged Mediterranean catchments using a simple stochastic hydrological rainfall-runoff model. Journal of Hydrology, 347: 132-142.
Artimani, M., Zeinivand, H., and Tahmasebipour, N. (2019). Performance evaluation of SRM and HBV model in simulation of snowmelt runoff in Bujin Basin. Iran-Water Resources Research, 15(2): 228-241. (In Farsi)
Binaman J., and Shoemaker C.A. (2005). An analysis of high-flow sediment event data for evaluating model performance. Journal of Hydrological Processes, 19: 605-620.
Breiman, L. (1984). Classification and regression trees CA, Wadsworth International Groups. Handbook of data mining and knowledge discovery. (PP. 248-276).
Breiman, L. (2001). Random forests. Machine Learning, 45(1): 5-32.
Chang, T.K., Talei, A., Alaghmand, S., and Ooi, M. P.-L. (2017). Choice of rainfall  inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. Journal of Hydrology, 545: 100–108
Chow, V.T., Maidment, D.R., and Mays, L.W. (1988). Applied Hydrology. McGraw-Hill Series in Water Resources and Environmental Engineering. McGraw-Hill: New York. PP 572.
Davanlou Tajbakhsh, A., Nourani, V., and Molajou, A. (2019). Hybrid Wavelet-M5 modeling in rainfall-runoff process forecast. Iran-Water Resources Research, 15(2): 1-10 (In Farsi)
Driessen, T.L.A., Hurkmans, R.T.W.L., Terink, W., Hazenberg, P., Torfs, P.J.J.F., and Uijlenhoet, R. (2010).The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model, Hydrology and Earth System Sciences, 14: 651–665,
El-Shafie, A., Taha, M.R., and Noureldin, A. (2007). A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resource Management, 21(3): 533-556.
Fathabadi, A., Salajegheh, A., and Mahdavi, M. (2009). Streamflow forecasting using neuro-fuzzy and time series methods. Iranian Journal of Watershed Management Science and Engineering, 2(5): 21-30. (In Farsi)
Ghorbani, M.A., Deo, R.C., Kim, S., Hasanpour Kashani, M., Karimi, V., and Izadkhah, M. (2020). Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia. Soft Computing, 24: 12079- 12090.
Hassanzadeh, Y., Abdi Kordani, A., Shafiei Najd, M., and Khoshtinat, S. (2015). Daily streamflow forecasting of Nooranchay River using the hybrid model of artificial neural networks- principal component analysis. Water and Soil Science, 25(3): 53-63. (In Farsi)
Heidler, L.M. (2015). Evaluation of Different Hydrological Models in Data Scarce Regions on the Island of Ceram, Indonesia. Technische Universitat Munchen. Faculty of Civil, Geo and Environmental Engineering. Master of Science Program Environmental Engineering.
Hussain, D., and Khan, A.A. (2020). Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Science Informatics, 13: 939- 949.
Jaiswal, R.K., Ali, S., and Bharti, B. (2020). Comparative evaluation of conceptual and physical rainfall- runoff models. Applied Water Science, 10, 48. doi:10.1007/s13201-019-1122-6
Konz, M., and Seibert, J. (2010). On the value of glacier mass balances for hydrological model calibration. Journal of Hydrology, 385(1-4): 238- 246.
Lorrai, M., and Sechi, M.G. (1995). Neural nets for modeling rainfall-runoff transformation, Water Resources Management, 9: 299 - 313.
Maneshdavi, A., Nikbakht Shahbazi, A., Fathian, H. (2018). Rainfall-runoff continuous simulation in Abolabbas watershed using SMA by HEC-HMS. Iranian Journal of Soil and Water Research, 49(2): 317-327. (In Farsi)
Moghaddam Nia, A., Almasi, P., Khalighi Sigaroodi, S., Salajeghe, A., and Soltani Koopaei, S. (2021). Performance evaluation of WetSpa hydrological model for runoff simulation in semi-arid climatic conditions (Case study: Menderjan Basin). Iranian Journal of Soil and Water Research, doi: 10.22059/ijswr.2021.315031.668827. (In Farsi)
Mohammadivand, M., Araghinejad, S., Ebrahimi, K., and Modaresi, F. (2019). Performance evaluation of AWBM, Sacramento and SimHyd models in runoff simulation of the Amameh Watershed using automatic calibration optimization method of Genetic Algorithm. Iranian Journal of Soil and Water Research, 50(7): 1759-1769. (In Farsi)
Mohammadi, M., Vagharfard, H., Mahdavi Najafabadi, R., Daneshkar Arasteh, P., and Nazemosadat, M. (2021). Rainfall-runoff modelling of coastal watersheds near the Strait of Hormuz using data mining. Iranian Journal of Soil and Water Research, doi: 10.22059/ijswr.2021.309641.668732. (In Farsi)
Nazaripooya, H., Kardavani, P., Farajirad, A. (2015). Calibration and evaluation of hydrological models, IHACRES and SWAT models in runoff simulation. Journal of Spatial Analysis Environmental Hazards, 2(2): 99-112. (In Farsi)
Nikpour, M., Sanikhani, H., Mahmodi Babelan, S., and Mohammadi, A. (2017). Application of LS-SVM, ANN, WNN and GEP in rainfall-runoff modeling of Kiyav-Chay River. Iranian Journal of Ecohydrology, 4(2 ): 627-639. (In Farsi)
Partovyan, A., Nourani, V., and Aalami, M. (2018). Noise injection– denoising techniques to improve artificial intelligence-based rainfall– runoff modeling. Water Engineering, 36: 81-94. (In Farsi)
Pham, L.T., Luo, L., and Finley, A.O. (2020). Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds. Hydrology and Earth System Sciences Discussions, https://doi.org/10.5194/hess-2020-305.
Phomcha, P., Wirojanagud, P., Vangpaisal, T., and Thaveevouthti, T. (2011). Suitability of SWAT model for simulating of monthly streamflow in Lam Sonthi Watershed. The Journal of Industrial Technology, 7(2): 49- 56.
Radchenko, I., Breuer, L., Forkutsa, I., and Frede, H.G. (2014). Simulating water resource availability under data scarcity- a case study for the Ferghana Valley (Central Asia). Water, 6(11): 3270–3299.
Ren ,W.W., Yang, T., Huang, C.S., Xu, C., and Shao, Q.X. (2018). Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian Neural Network. Stochastic Environmental Research and Risk Assessment, 32: 3381–3396.
Santhi, C., Arnold, J.G., Williams, J., Dugas, W.A., and Hauck L. (2001).Validation of the SWAT model on a large river basin with point and nonpoint sources. Journal of the American Water Resources Association, 37(5): 1169–1188.
Seibert, J. (2000). Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology and Earth System Sciences Discussions, 2: 215- 224.
Seyedian, S., Bagherpour, M., Fathabadi, A., and Mohammadi, A. (2019). Runoff prediction using black and gray box models. Iran-Water Resources Research, 14(5): 204-219. (In Farsi)
Shafeizadeh, M., Fathian, H., and Nikbakht Shahbazi, A. (2019). Continuous rainfall-runoff simulation by artificial neural networks based on efficient input variables selection using partial mutual information (PMI) algorithm. Iran-Water Resources Research, 15(2): 144-161. (In Farsi)
Yaghoubi, M., and Massah Bavani, A. (2014). Sensitivity analysis and comparison of capability of three conceptual models HEC-HMS, HBV and IHACRES in simulating continuous rainfall-runoff in SEMI-ARID Basins. Journal of the Earth and Space Physics, 40(2): 153-172. (In Farsi)
Zeinali, M., Golabi, M., Sharifi, M., and Hafezparast, M., (2020). Evaluation of artificial intelligence models in river flow modeling (Case study: Gamasiab River). Watershed Engineering and Management, 11(4): 941-954. (In Farsi)