Flood susceptibility mapping of Northeast coastal districts of Tamil Nadu India using Multi-source Geospatial data and Machine Learning techniques

Flooding is one of the most challenging and important natural disasters to predict, it is becoming more frequent and more intense. The study area is badly damaged by devastating flood in 2015. We assessed the flood susceptibility to northern coastal area of Tamil Nadu using various machine learning algorithms such as Gradient Boosting Machine (GBM), XGBoost (XGB), Rotation Forest (RTF), Support Vector Machine (SVM), and Naive Bayes (NB). Google Earth Engine (GEE) is used to demarcate flooded areas using Sentinel-l and other multi-source geospatial data to generate influential factors. Recursive Feature Elimination (RFE) removes weak factors in this study. The flood susceptibility resultant map is classified into five classes: very low, low, moderate, high, and very high. The GBM algorithm attained high classification accuracy with an area under the curve (AUC) value of 92%. The study area is urbanized and vulnerable identifying flood inundation useful for effective planning and implementation

Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach

A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu