Data from: Forecasting landslides using community detection on geophysical satellite data
Data files
Jun 08, 2023 version files 46.12 MB
-
Coords_01.csv
-
Coords_02.csv
-
Coords_03.csv
-
Coords_04.csv
-
Coords_05.csv
-
Coords_06.csv
-
Coords_07.csv
-
Coords_08.csv
-
Coords_09.csv
-
Coords_10.csv
-
EdgeList_01.csv
-
EdgeList_02.csv
-
EdgeList_03.csv
-
EdgeList_04.csv
-
EdgeList_05.csv
-
EdgeList_06.csv
-
EdgeList_07.csv
-
EdgeList_08.csv
-
EdgeList_09.csv
-
EdgeList_10.csv
-
README.md
-
SlopeWeightedEdges_01.csv
-
SlopeWeightedEdges_02.csv
-
SlopeWeightedEdges_03.csv
-
SlopeWeightedEdges_04.csv
-
SlopeWeightedEdges_05.csv
-
SlopeWeightedEdges_06.csv
-
SlopeWeightedEdges_07.csv
-
SlopeWeightedEdges_08.csv
-
SlopeWeightedEdges_09.csv
-
SlopeWeightedEdges_10.csv
-
VelocityWeightedEdges_01.csv
-
VelocityWeightedEdges_02.csv
-
VelocityWeightedEdges_03.csv
-
VelocityWeightedEdges_04.csv
-
VelocityWeightedEdges_05.csv
-
VelocityWeightedEdges_06.csv
-
VelocityWeightedEdges_07.csv
-
VelocityWeightedEdges_08.csv
-
VelocityWeightedEdges_09.csv
-
VelocityWeightedEdges_10.csv
Abstract
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 elevation model and ground surface deformation (current rheological state) from interferometric synthetic aperture radar (InSAR). We use multilayer modularity optimization to identify strongly-connected clusters of nodes (communities) and are able to identify both the location of Mud Creek and nearby creeping landslides which have not yet failed. We develop a metric, community persistence, to quantify patterns of ground deformation leading up to failure, and find that this metric increases from a baseline value in the weeks leading up to Mud Creek's failure. These methods promise as a technique for highlighting regions at risk of catastrophic failure.
Usage notes
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.