Each year thousands of people and millions of dollars in assets are affected by flooding in Senegal; over the next decade, the frequency of such extreme events is expected to increase. However, no publicly available digital flood maps, except for a few aerial photos or post - disaster assessments from UNOSAT, could be found for the country. This report tested an experimental method for assessing the socio - physical vulnerability y of Senegal using high capacity remote sensing, machine learning, new social science, and community engagement. This scientific approach to flood analysis developed in this report is much faster and much more responsive than traditional flood mapping, at only a fraction of the cost. First Cloud to Street‘s customized water detection algorithms were run for several publicly available satellites (MODIS, Landsat) to map major floods from the last 30 years and second machine learning approach to hydrology in Google Earth Engine was trained on the maps of past floods. Third, a Principal Component Analysis, which r a n on custom - designed Census Senegal variables, revealed five underlying dimensions of social vulnerability to flooding. Overall, the research predicts s a floodplain in Senegal of 5, 596 km 2, 30 % of which is high - risk zone where over 97,000 people live. Approximately 5 million people live in the 30 arrondissements that have very high social vulnerability profiles compared to other arrondissements. In a Fu, true version, this risk platform could be set to stream satellite imagery public and other sensors, so that the vulnerability analysis for Senegal can be updated with the mere refresh of a browser page - no downloading is required.
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