Abstract

A conceptual framework is proposed, to identify flood affected locations that should be considered in order to lessen the consequences of naturally occurring disaster. Sentinel-1 data are used to evaluate the performance of automatic Otsu’s method and machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), CART, Minimum Distance (MD), K-nearest neighbour (KNN) and KD Tree KNN (KD-KNN)) to characterise flooded region. The study provided a holistic spatial assessment of flood inundation in the region due to impact of the extreme precipitation. The most adequate performance based on compound value is achieved by KNN (C<inf>v</inf>= 2) followed by SVM (C<inf>v</inf>= 2.25) ML model and Otsu’s thresholding method (C<inf>v</inf>= 2.5). The validation site results reveal that Vertical transmit and Vertical received (VV) polarization performs significantly better than Vertical transmit and Horizontal received (VH) polarization. The most accurate flood extent produced by Otsu’s thresholding method (overall accuracy of 94.98%) and MD (overall accuracy of 88.98%) are used to evaluate the indicative number of individuals and buildings at risk within the study areas using Gridded Population of the World Version 4 (GPWv4), Global ML Building Footprints by Microsoft and OpenStreetMap building data.