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

Abstract

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.