Utilizing SAR, GIS, and Deep Learning for Flood Susceptibility Mapping: A Case Study of a Flood in Mozambique

The data that support the findings of this study were derived from the following resources available in the public domain: [[scihub.copernicus.eu/ and https://earthexplorer.usgs.gov/] . This study integrated the geographic information system, SAR data, and two deep learning techniques; namely, the convolutional neural network (CNN) and Group Method of Data Handling (GMDH) network model techniques, to generate a flood susceptibility map (FSM).

本研究结论所依托的数据均取自以下公开可获取资源：https://scihub.copernicus.eu/ 与 https://earthexplorer.usgs.gov/。本研究整合了地理信息系统、合成孔径雷达（SAR）数据，以及两类深度学习技术，即卷积神经网络（Convolutional Neural Network，简称CNN）与数据处理群方法（Group Method of Data Handling，简称GMDH）网络模型，以此生成洪水敏感性制图（Flood Susceptibility Map，简称FSM）。