干旱区生态环境知识资源中心(Arid): Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

Arid

DOI	10.3390/rs12172695
	Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach
	Elmahdy, Samy; Ali, Tarig; Mohamed, Mohamed
通讯作者	Elmahdy, S
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:17
英文摘要	In an arid region, flash floods (FF), as a response to climate changes, are the most hazardous causing massive destruction and losses to farms, human lives and infrastructure. A first step towards securing lives and infrastructure is the susceptibility mapping and predicting of occurrence sites of FF. Several studies have been applied using an ensemble machine learning model (EMLM) but measuring FF magnitude using a hybrid approach that integrates machine learning (MCL) and geohydrological models have not been widely applied. This study aims to modify a hybrid approach by testing three machine learning models. These are boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) for FF susceptibility mapping at the northern part of the United Arab Emirates (NUAE). This is followed by applying a group of accuracy metrics (precision, recall and F1 score) and the receiving operating characteristics (ROC) curve. The result demonstrated that the BRT has the highest performance for FF susceptibility mapping followed by the CART and NBT. After that, the produced FF map using the BRT was then modified by dividing it into seven basins, and a set of new FF conditioning parameters namely alluvial plain width, basin gradient and mean slope for each basin was calculated for measuring FF magnitude. The results showed that the mountainous and narrower basins (e.g., RAK, Masafi, Fujairah, and Rol Dadnah) have the highest probability occurrence of FF and FF magnitude, while the wider alluvial plains (e.g., Al Dhaid) have the lowest probability occurrence of FF and FF magnitude. The proposed approach is an effective approach to improve the susceptibility mapping of FF, landslides, land subsidence, and groundwater potentiality obtained using ensemble machine learning, which is used widely in the literature.
英文关键词	NUAE flash flood BRT CART naive Bayes tree geohydrological model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000569793200001
WOS关键词	LOGISTIC-REGRESSION ; WATER-RESOURCES ; DECISION TREES ; GIS ; CLASSIFICATION ; PREDICTION ; BIVARIATE ; RAINFALL ; ALGORITHMS ; WEIGHTS
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/2XILL650/326327
作者单位	[Elmahdy, Samy; Ali, Tarig] Amer Univ Sharjah, Coll Engn, Civil Engn Dept, GIS & Mapping Lab, POB 26666, Sharjah, U Arab Emirates; [Mohamed, Mohamed] United Arab Emirates Univ, Civil & Environm Engn Dept, POB 15551, Al Ain, U Arab Emirates; [Mohamed, Mohamed] United Arab Emirates Univ, Natl Water Ctr, POB 15551, Abu Dhabi, U Arab Emirates
推荐引用方式 GB/T 7714	Elmahdy, Samy,Ali, Tarig,Mohamed, Mohamed. Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach[J],2020,12(17).
APA	Elmahdy, Samy,Ali, Tarig,&Mohamed, Mohamed.(2020).Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach.REMOTE SENSING,12(17).
MLA	Elmahdy, Samy,et al."Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach".REMOTE SENSING 12.17(2020).