Assessing Flood Risk Potential using Advanced Statistical and Machine Learning Models in the Lower Gangetic Floodplain Region, India

The number of natural disasters is increasing in different parts of the world due to the eccentricity of the weather. These natural disasters have made the daily lives of people very miserable. Flood organization in floodplain regions is much higher than in other natural disasters, and due to a lack of proper flood control plans and identification of appropriate flood areas, local people are easily exposed to flood disasters. In India, various parts of the Bhagirathi-Hooghly River basin are prone to heavy rains and loss of carrying capacity due to overflowing of the riverbanks, causing severe floods, which increase the amount of social, financial and socio-economic damage to the people. Due to the need for a specific flood control and planning system, the region experiences flood every year. Through this article and identification of the flood-susceptible regions of the Bhagirathi basin, flood-vulnerable and risk areas have also been identified separately for disaster assessment and flood protection measures. Based on 12 flood inventory conditioning parameters, a susceptibility analysis is performed through bivariate, multivariate, and machine learning models. Parametric vulnerability indices develop by susceptibility, exposure, and resilience on a block-wise small scale to assess socio-economic damage due to floods. Based on that, the spatial distribution of flood risk has been developed by amalgamating the pixel-based flood susceptibility obtained by raster normalization and the flood vulnerability generated by the block-based spatial distribution using the ArcGIS interface. The results obtained from these observations will help policymakers develop holistic, sustainable development through flood prevention and control plans by considering the local environment and climate at the micro-scale of the block level.