Forecasting landslides using community detection on geophysical satellite data

As a result of extreme weather conditions, such as heavy precipitation, natural hillslopes can fail dramatically; these slope failures can occur on a dry day due to time lags between rainfall and pore-water pressure change at depth, or even after days to years of slow-motion. While the pre-failure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation (creep) to runaway failure. We use a network science method–multilayer modularity optimization–to investigate the spatiotemporal patterns of  deformation in a region near the 2017 Mud Creek, California landslide. We transform satellite radar data from the study site into a spatially-embedded network in which the nodes are patches of ground and the edges connect the nearest neighbors, with a series of layers representing consecutive transits of the satellite.  Each edge is weighted by the product of the local slope (susceptibility to failure) measured from a digital ..., , Uses R to create the networks and Matlab to run the community detection algorithm. The code can be found on https://github.com/vddesai-97/networkLandslide.git, which uses the community detection algorithim from https://github.com/GenLouvain/GenLouvain. The dataset contains 10 edge lists, 10 corresponding spatial grids, and 10 resulting community detection results. ,