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Assessment of flood risk zonation and landscape vulnerability to flood are crucial perspective in flood risk management. It is a standout amongst the most genuine catastrophic events in North East, India. The mighty Brahmaputra being one of the significant streams of Asia, is a trans-boundary waterway which flows through China, India and Bangladesh. Floods are an exceptionally regular event during the monsoon season. Deforestation in the Brahmaputra watershed has brought increased siltation levels, flash floods and soil erosion in basic downstream natural environment. To detect changes in land cover and NDVI rapidly and precisely, we have utilized remote sensing technology and Geographic Information Systems (GIS). In this paper, Landsat 8 Operational Land Imager (OLI) data were utilized to access landscape vulnerability to flood inundation and flood risk in Kamrup district of Assam, India. Flood inundation outline was prepared based on water and land pixels on images. We have proposed an automated flood alert generating and continuous monitoring system which analyses the recent images by extracts the number of geo-tagged pixels of water body (extracts area and contour of water and land regions) and generate when the number of pixels of water crosses the threshold, our system generates an alert. Based on the area of the water regions on the map we could estimate flood affected regions comparing with the existing data of land and water cover regions reported by the government and there by alert the officials for preventive measures. Different metrics were experimented to extract more accurate area of water logging regions. With the historical information of rainfall and area of water logging regions we could apply machine learning models to predict the possible inundation in next 48 hours. This will be more effective to move people to safe high-altitude regions during the time of flood.
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