Eco-hydrological estimation of event-based runoff coefficient using artificial intelligence models in Kasilian watershed
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ORIGINAL PAPER
Eco-hydrological estimation of event-based runoff coefficient using artificial intelligence models in Kasilian watershed, Iran Hossein Pourasadoullah1 • Mehdi Vafakhah2 Alireza Moghaddam Nia4
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Baharak Motamedvaziri1 • Hossein Eslami3
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Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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, U 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. Keywords Artificial neural network Normalized difference vegetation index Principal component analysis Runoff management Soil and water conservation
1 Introduction In the last decades, accurate and timely estimation of runoff status is one of the concerns of watershed management (Heathcote 2009). Inadequate estimation of runoff from watersheds hinders optimal soil conservation and water resources management (Michaud and Sorooshian & Mehdi Vafakhah [email protected] 1
Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran Province, Iran
3
Faculty of Agriculture, Shoushtar Branch, Islamic Azad University, Shoushtar, 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
1994). Considering the increasing floods and dangers that threaten human societies and infrastructures, it is important to improve the rainfall-runoff prediction and measurement of runoff (Hua et al. 2020). Many studies have determined the Runoff Coefficient (RC) (Barazzuoli et al. 1989). Some hydrological methods have used weather radar and a downstream runoff sensors (Ahm et al. 2013), distribution of peak flow (Gottschalk and Weingartner 1998), rainfall– runoff response (Rodrı´guez-Blanco et al. 2012), TOPMODEL (Xiong and Guo 2
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