干旱区生态环境知识资源中心(Arid): Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

Arid

DOI	10.1007/s11069-024-06550-z
	Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas
	Bammou, Youssef; Benzougagh, Brahim; Igmoullan, Brahim; Ouallali, Abdessalam; Kader, Shuraik; Spalevic, Velibor; Sestras, Paul; Billi, Paolo; Markovic, Slobodan B.
通讯作者	Kader, S
来源期刊	NATURAL HAZARDS
ISSN	0921-030X
EISSN	1573-0840
出版年	2024
卷号	120 期号:8 页码:7787-7816
英文摘要	This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where 1 indicates a flood-prone area and 0 a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.
英文关键词	Flood susceptibility GIS Machine learning Factor importance Tensift watershed
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001190067600001
WOS关键词	RISK-ASSESSMENT ; MODELS
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/2XILL650/404957
推荐引用方式 GB/T 7714	Bammou, Youssef,Benzougagh, Brahim,Igmoullan, Brahim,et al. Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas[J],2024,120(8):7787-7816.
APA	Bammou, Youssef.,Benzougagh, Brahim.,Igmoullan, Brahim.,Ouallali, Abdessalam.,Kader, Shuraik.,...&Markovic, Slobodan B..(2024).Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas.NATURAL HAZARDS,120(8),7787-7816.
MLA	Bammou, Youssef,et al."Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas".NATURAL HAZARDS 120.8(2024):7787-7816.