干旱区生态环境知识资源中心(Arid): Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context

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DOI	10.1080/10106049.2022.2129818
	Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context
	Eloudi, Hasna; Reddad, Hanane; Hssaisoune, Mohammed; Estrany, Joan; Krimissa, Samira; Elaloui, Abdenbi; Namous, Mustapha; Ouatiki, Hamza; Aboutaib, Fatima; Ouayah, Mustapha; Jadoud, Mourad; Edahbi, Mohamed; Bouchaou, Lhoussaine
通讯作者	Eloudi, H
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2022
卷号	37 期号:27 页码:17435-17464
英文摘要	Gully erosion is a complex socio-environmental issue that has a negative influence on natural resources and has significant economic costs. This study examined the performance of two ensemble models based on multicriteria decision making (MCDM) analysis, analytic hierarchy process (AHP), weight of evidence (WoE) and random forest (RF) for spatiotemporal monitoring of gully erosion sensitivity (GES) from 1988 to 2019 as well as a projection for 2040 in a semi-arid area. The findings revealed that the vulnerable areas significantly raise between 1988 and 2040 (> 27% of the study area since 2019), in perfect alignment with a rapid deterioration of the vegetation cover (-16%), a general decrease in rainfall (-25% since 2019), and an increase in land surface temperature (LST) average (30 degrees-37 degrees approximatively). Finally, the area under curve (AUC) value revealed a high prediction performance for both developed models (AUC = 0.888 for WoE-RF and 0.886 for MCDM-WoE-AHP).
英文关键词	Ensemble of models analytical hierarchy process WoE random forest climate change
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000870187000001
WOS关键词	LANDSLIDE SUSCEPTIBILITY ; SOIL-EROSION ; DECISION-MAKING ; SEDIMENT YIELD ; HIGH-ATLAS ; VALIDATION ; IMPACTS ; REGION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392901
推荐引用方式 GB/T 7714	Eloudi, Hasna,Reddad, Hanane,Hssaisoune, Mohammed,et al. Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context[J],2022,37(27):17435-17464.
APA	Eloudi, Hasna.,Reddad, Hanane.,Hssaisoune, Mohammed.,Estrany, Joan.,Krimissa, Samira.,...&Bouchaou, Lhoussaine.(2022).Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context.GEOCARTO INTERNATIONAL,37(27),17435-17464.
MLA	Eloudi, Hasna,et al."Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context".GEOCARTO INTERNATIONAL 37.27(2022):17435-17464.