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While floods are considered as costliest natural disasters globally, their impact on developing countries is disproportionately high in terms of lost lives and irreversible property damage. In India, 27. 75 percentage of the countrys population is exposed to high flood risk areas out of which majority of the population are concentrated in the Ganges- Brahmaputra plain. The Brahmaputra River has a history of yearly flooding, and over 674 deaths and more than 1 million worth of property loss were reported during the years 20122023 due to the rivers annual flooding. To mitigate the forthcoming effects of flooding, we introduce a spatial flood forecasting system in the middle Brahmaputra basin that predicts the spatial locations as well as magnitude of the impending flood with the help of multitemporal Synthetic Aperture Radar images. By taking the advantage of the abundant Earth Observation data available, we demonstrated the forecasting system that predict locations experiencing maximum flooding in a single flood plain of the middle Brahmaputra basin. In the initial phase, 169 SAR images from 2006-2022 were processed to create an inventory of flood extents and levels in a 1Sq. Km spatial zones. The forecasting model has been established using LSTM network and is calibrated to predict the upcoming flood hit locations as well as the flood depths in predicted spatial zones. This paper presents the procedures and findings of the proposed forecasting model along with potential future directions of this work.
Surampudi et al. (Fri,) studied this question.