干旱区生态环境知识资源中心(Arid): Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)

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DOI	10.1016/j.pce.2019.05.002
	Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)
	Ahmadi, Mehdi 1; Moeini, Abolfazl 1; Ahmadi, Hassan 2; Motamedvaziri, Baharak 1; Zehtabiyan, Gholam Reza 3
通讯作者	Moeini, Abolfazl
来源期刊	PHYSICS AND CHEMISTRY OF THE EARTH
ISSN	1474-7065
EISSN	1873-5193
出版年	2019
卷号	111 页码:65-77
英文摘要	The catchment area is essentially a heterogeneous dynamic, time and space hydrological system, and so the process of rainfall-runoff transmission in the catchment area is a very complex phenomenon. The temporal and spatial changes in the catchment characteristics, uncertainties in rainfall patterns, and a large number of parameters that alter the rainfall in to runoff, are the main sources of complexity in such relationships. Hydrological models are vital and exigent tools for water resources and environmental planning and management. In present study three models of SWAT, IHACRES and ANN were used on a daily, monthly and annual basis in the Kan watershed, which located in the west part of Tehran, Iran. The results showed that the performance of the three considered models are generally suitable for rainfall-runoff process simulation, however, ANN model showed a better performance for daily, monthly, and annual flow simulations compared with other two models (NSE= 0.86, R-2= 0.87, RMSE= 2.2, MBE= 0.08), and particularly for the simulation of maximum and minimum flow values. In addition, the performance of SWAT model (NSE= 0.65, R-2= 0.68, RMSE= 3.3, MBE= -0.168) was better than the IHACRES model (NSE= 0.57, R-2= 0.58, RMSE= 3.7, MBE= 0.049). However, the results of the IHACRES model were still acceptable.
英文关键词	Arid and semiarid regions Kan watershed Hydrology models Runoff simulation
类型	Article
语种	英语
国家	Iran
收录类别	SCI-E
WOS记录号	WOS:000471830500006
WOS关键词	RIVER-BASIN ; HYDROLOGICAL MODELS ; CLIMATE-CHANGE ; UNCERTAINTY ; CATCHMENT ; IMPACT ; FLOW ; PARAMETERIZATION ; OPTIMIZATION ; CALIBRATION
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217959
作者单位	1.Islamic Azad Univ, Dept Forest Range & Watershed Management, Sci & Res Branch, Tehran, Iran; 2.Univ Tehran, Coll Agr & Nat Resources, Karaj, Iran; 3.Univ Tehran, Dept Nat Resources, Karaj, Iran
推荐引用方式 GB/T 7714	Ahmadi, Mehdi,Moeini, Abolfazl,Ahmadi, Hassan,et al. Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)[J],2019,111:65-77.
APA	Ahmadi, Mehdi,Moeini, Abolfazl,Ahmadi, Hassan,Motamedvaziri, Baharak,&Zehtabiyan, Gholam Reza.(2019).Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran).PHYSICS AND CHEMISTRY OF THE EARTH,111,65-77.
MLA	Ahmadi, Mehdi,et al."Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)".PHYSICS AND CHEMISTRY OF THE EARTH 111(2019):65-77.