Estimation of event based runoff coefficient using artificial intelligence models (Case study: Kasilian watershed)

Document Type : Research Article


1 Science and Research Branch, Islamic Azad University, Tehran, Iran

2 خیابان حافظ-کوچه هاشمی نژاد شمالی-کوی گلشن ۴

3 Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, College of Agriculture & Natural Resources, University of Tehran, Daneshkadeh Ave., karaj, Iran

5 Faculty of Agriculture, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran


In this research, estimation of the Runoff Coefficient (RC) is carried out depending on land cover. Initially, RC modeling was performed using 54 hourly rainfall and corresponding runoff data during the period 1987–2010 in the Kasilian watershed. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models and effective factors including rainfall intensity, Φ index (the average loss), five-day previous rainfall and Normalized Difference Vegetation Index (NDVI) were used to estimate RC. The results showed that the ANN model was more efficient than the other two models and had Mean Bias Error (MBE), Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and Normalized Root Mean Square Error (NRMSE) equal to 0.08, 0.85, 0.84 and 0.37, respectively for the training phase and 0.12, 0.76, 0.74 and 0.47 for the test phase. In general, it is suggested that RC plays a major role in hydrological mechanisms and flooding. Thus, optimal estimation of RC can be helpful in better management of soil and water conservation and erosion and sediment management in this area.


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Volume 8, Issue 2
July 2021
Pages 499-512
  • Receive Date: 30 January 2021
  • Revise Date: 15 June 2021
  • Accept Date: 15 June 2021
  • First Publish Date: 22 June 2021