Identification and classification of landslide susceptible zone using geospatial techniques and machine learning models

Abstract

Recent works on landslide-susceptibility mapping for Idukki are based on a short-term dataset with few landslide features resulting in reactive predictions. Hence, the objective is to identify landslide-sensitive areas in Idukki precisely using geospatial and machine-learning techniques. This research considers a large dataset corresponding to 14 years with 23 landslide-influencing features for increased accuracy. The predominant features include lithology, height above the nearest drainage, relative relief, daily rainfall, groundwater level, vegetation index, built-up index, and soil-related features. The Topographic features are extracted from Shuttle Radar Topography Mission Global 30 m. The Machine learning models, namely, Logistic-Regression, Support-Vector-Machine, Decision-Tree, Random-Forest, and eXtreme-Gradient-Boost, are trained and tested. The predictive capability of these models is assessed using Area Under the Curve and is compared. The results show the prediction of vulnerable areas, to reduce loss of life and infrastructural damage. Further, feature dependency analysis is performed to analyze the feature sensitivity on landslides.