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• W2981595931 abstract "Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds." @default.
• W2981595931 created "2019-11-01" @default.
• W2981595931 creator A5038585011 @default.
• W2981595931 date "2018-06-30" @default.
• W2981595931 modified "2023-09-24" @default.
• W2981595931 title "Analysis of large-scale flood inundation area using optimal topographic factors" @default.
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• W2981595931 doi "https://doi.org/10.3741/jkwra.2018.51.6.481" @default.
• W2981595931 hasPublicationYear "2018" @default.
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