干旱区生态环境知识资源中心(Arid): Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Arid

DOI	10.1007/s11629-018-4875-8
	Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran
	Ghanjkhanlo, Hojat 1; Vafakhah, Mehdi 1; Zeinivand, Hossein 2; Fathzadeh, Ali 3
通讯作者	Vafakhah, Mehdi
来源期刊	JOURNAL OF MOUNTAIN SCIENCE
ISSN	1672-6316
EISSN	1993-0321
出版年	2020
卷号	17 期号:7 页码:1712-1723
英文摘要	Direct measurement of snow water equivalent (SWE) in snow-dominated mountainous areas is difficult, thus its prediction is essential for water resources management in such areas. In addition, because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution, statistical models are not usually able to present acceptable results. Therefore, applicable methods that are able to predict nonlinear trends are necessary. In this research, to predict SWE, the Sohrevard Watershed located in northwest of Iran was selected as the case study. Database was collected, and the required maps were derived. Snow depth (SD) at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling (LHS), and snow density at 18 points were randomly measured, and then SWE was calculated. SWE was predicted using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and regression methods. The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method. Moreover, based on most of the efficiency criteria, the efficiency of ANN, ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern. However, there were no significant differences between the two methods of ANN and ANFIS in SWE prediction. Data of both two sampling patterns had the highest sensitivity to the elevation. In addition, the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature, respectively.
英文关键词	ANFIS ANN Latin hypercube sampling Systematic random sampling Snow water equivalent Snow depth
类型	Article
语种	英语
国家	Iran
收录类别	SCI-E
WOS记录号	WOS:000540675700002
WOS关键词	SPATIAL-DISTRIBUTION ; TREE MODELS ; MACHINE ; CLIMATE ; DEPTH
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319667
作者单位	1.Tarbiat Modares Univ, Fac Nat Resources, Dept Watershed Management Engn, Noor 64414356, Iran; 2.Lorestan Univ, Dept Watershed Management Engn, Khorramabad 6815144316, Iran; 3.Ardakan Univ, Fac Agr & Nat Resources, Ardakan 8951895491, Iran
推荐引用方式 GB/T 7714	Ghanjkhanlo, Hojat,Vafakhah, Mehdi,Zeinivand, Hossein,et al. Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran[J],2020,17(7):1712-1723.
APA	Ghanjkhanlo, Hojat,Vafakhah, Mehdi,Zeinivand, Hossein,&Fathzadeh, Ali.(2020).Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran.JOURNAL OF MOUNTAIN SCIENCE,17(7),1712-1723.
MLA	Ghanjkhanlo, Hojat,et al."Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran".JOURNAL OF MOUNTAIN SCIENCE 17.7(2020):1712-1723.