Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: A case study in Uttarakhand (India)

July 2013 unleashed one of the worst floods in the history of mankind for the North Indian state of Uttarakhand. Four years later and still little can be said about the state of affairs in the region that is still recuperating from the loss of lives, property and biodiversity. This paper attempts to utilize the state-of-the-art machine learning techniques for flood susceptibility assessment in the flood ravaged district of Chamoli. Remote Sensing and Geographic Information Systems hand-in-glove with procured satellite images unravel the mystery behind the district's traits that conditioned it for the final encounter with the unfortunate disaster. The predictive accuracy of the proposed particle swarm optimized-support vector machine was compared against various conventional models like Random Forest, Neural Networks. Outperforming them, it achieved an accuracy of 96.55%. Flood susceptibility map for the study area was generated indicating that approximately 1,832.3 sq. km area is highly susceptible to floods.