Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model

Landslide susceptibility mapping and landslide hazard are approaches used to assess the potential for landslides predict occurrence of landslides, respectively. We evaluated tested a limit equilibrium approach produce local-scale, multi-temporal geographic information system-based map that utilized satellite soil moisture data, strength hydrologic high-resolution (1.5 m) LiDAR-derived digital elevation map. The final was validated temporally spatially using four study sites at known locations failure dates. resulting product correctly indicated low factor safety values on dates occurred. Also, we produced regional-scale logistic regression machine learning model 15 variables derived from geomorphology, properties, land-cover data. area under curve receiver operating characteristic accuracy model, which yielded success rate 0.84. show publicly available can be created will close-to-real-time predictive provides an understanding into evolution development spatially, whereas indicates probability occurring specific locations. When in tandem, two models complementary each other. Specifically, identifies most susceptible while predicts when may occur within identified area.