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Landslide Susceptibility Mapping for Road Corridors by Using a Combined Interferometry SAR and Machine Learning Techniques
Arsyad A.
Geotechnical Special Publication
Abstract
Landslides comprise about 42% of the entire natural hazards in Indonesia, claiming one-third of the annual economic losses caused by natural disasters. To mitigate their severe impacts, a landslide susceptibility map (LSM) with actual, continuous, and accurate information about landslide occurrences and their likelihood in a particular area is urgently needed. Therefore, this research presents a reliable landslide susceptibility mapping with combined interferometry synthetic aperture radar (In-SAR) and machine learning (ML) techniques. A new framework of LSM by using In-SAR and ML applied to road infrastructure was proposed. The framework begins with the acquisition of a satellite imaging-based digital elevation model (DEM) of a particular road corridor, in which the landslide contributing factors (slopes, natural drainage networks, lithology, and rainfall) were rendered and overlaid by using GIS. Then, the ML was used to rate those contributing factors to the actual landslide occurrences. In a parallel way, the In-SAR was employed to obtain the ground movements in the road corridor from a series of SAR images derived from the Sentinel-1. Interferograms were then generated to produce ground movement maps. By combining the ground movement maps from the In-SAR and landslide ratings maps from the ML, a landslide susceptibility map was created. The applicability of the framework was investigated through a case study of landslide susceptibility in West Sulawesi, Indonesia.