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DOI	10.1007/s11269-020-02589-2
	Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions
	Allahbakhshian-Farsani, Pezhman; Vafakhah, Mehdi; Khosravi-Farsani, Hadi; Hertig, Elke
通讯作者	Vafakhah, M
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2020
卷号	34 期号:9 页码:2887-2909
英文摘要	The machine learning models (MLMs), including support vector regression (SVR), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and projection pursuit regression (PPR) are compared to traditional method i.e. nonlinear regression (NLR) in regional flood frequency analysis (RFFA). In this study, the Karun and Karkheh watersheds, which is located in the southwestern of Iran, with the same climatic and physiographic conditions are considered. Fifty-four hydrometric stations with a period of 21 years (1993-2013) are selected based on the instructions of U.S. Federal Agencies Bulletin 17 B were applied for RFFA. The generalized normal (GNO) probability distribution function (PDF) is selected by the L-moment method among five PDFs, including the GNO, generalized Pareto (GP), generalized logistic (GL), generalized extreme value (GEV) and Pearson type 3 (P CYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN ICYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN ICYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I) to estimate flood discharge for the expected return periods. Twenty-five predictor variables, such as physiographic, climatologic, geologic, soil and land use variables are extracted. Follow land, maximum 24-h rainfall, mean watershed slope, compactness coefficient, mean and maximum watershed elevation variables are recognized as the appropriate combination of input using gamma test (GT). The overall results indicate that the SVR, PPR, and MARS models in comparison to the NLR and BRT models have a better performance to estimate flood discharge with the expected return periods. Future, the SVR model based on radial basis function (RBF) kernel is chosen as the best model in terms of the mean of the Nash-Sutcliff coefficient (M-Ef) and the mean of relative root mean squared error (M-RMSEr) (i.e. 0.94 and 63.93, respectively) for different return periods.
英文关键词	Regionalization Data Driven models Land use Maximum instantaneous discharge L-moment Karun and Karkheh watersheds
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000544851900001
WOS关键词	SUPPORT VECTOR REGRESSION ; L-MOMENTS ; UNGAUGED SITES ; NEURO-FUZZY ; AT-SITE ; PREDICTION ; SELECTION
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324903
作者单位	[Allahbakhshian-Farsani, Pezhman; Vafakhah, Mehdi] Tarbiat Modares Univ, Fac Nat Resources, Dept Watershed Management Engn, POB 46414-356, Noor Mazandaran, Iran; [Khosravi-Farsani, Hadi] Shahrekord Univ, Dept Comp Engn, Shahrekord, Iran; [Hertig, Elke] Univ Augsburg, Fac Med, Augsburg, Germany
推荐引用方式 GB/T 7714	Allahbakhshian-Farsani, Pezhman,Vafakhah, Mehdi,Khosravi-Farsani, Hadi,et al. Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions[J],2020,34(9):2887-2909.
APA	Allahbakhshian-Farsani, Pezhman,Vafakhah, Mehdi,Khosravi-Farsani, Hadi,&Hertig, Elke.(2020).Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions.WATER RESOURCES MANAGEMENT,34(9),2887-2909.
MLA	Allahbakhshian-Farsani, Pezhman,et al."Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions".WATER RESOURCES MANAGEMENT 34.9(2020):2887-2909.