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Floods are one of the most common natural disasters, causing huge damage to infrastructure, economic activities, and human life every year. One of the most important tasks in flood disaster management is mapping areas that are vulnerable to flooding. As a result, reliable flood mapping is necessary to control floods effectively and lessen their negative impacts. Synthetic Aperture Radar (SAR) is appropriate for use in flood research because it uses microwave technology that can penetrate clouds. In this study, flooding information is extracted from Sentinel-1 (SAR) data and fed into a powerful tool of automatic image classification, Random Forest (RF) and uses accuracy was obtained using only 4 factors namely, Flooded area, Soil, Vegetation, and Water with 93.18% overall accuracy.
Myin et al. (Sat,) studied this question.
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