Enhancing flood risk assessment in northern Morocco with tuned machine learning and advanced geospatial techniques  

作　　者：MOUTAOUAKIL Wassima HAMIDA Soufiane SALEH Shawki LAMRANI Driss MAHJOUBI Mohamed Amine CHERRADI Bouchaib RAIHANI Abdelhadi 

机构地区：[1]EEIS Laboratory,ENSET of Mohammedia,Hassan Ⅱ University of Casablanca,Mohammedia,Morocco [2]IACS Laboratory,ENSET of Mohammedia,Hassan Ⅱ University of Casablanca,Mohammedia,Morocco [3]GENIUS Laboratory,SupMTI of Rabat,Rabat,Morocco [4]STIE Team,CRMEF Casablanca-Settat,Provincial Section of El Jadida,El Jadida,Morocco

出　　处：《Journal of Geographical Sciences》2024年第12期2477-2508,共32页地理学报（英文版）

摘　　要：Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.

关 键 词：remote sensing conditioning factors GIS flood susceptibility machine learning DEM 

分 类 号：P208[天文地球—地图制图学与地理信息工程] TV877[天文地球—测绘科学与技术]