Landslides in Forests around the World: Causes and Mitigation

© 2022 by the authors; CC BY-NC-ND license

shallow landslide; probability of occurrence; typhoon; data-driven model; Ningguo City; landslide susceptibility; antecedent effective precipitation; daily precipitation; hybrid landslide warning model; multibaseline InSAR techniques; Guang’an Village Landslide; InSAR; time-series analysis; post-event deformation mapping; root-soil composite; root reinforcement coefficient; shear strength parameters; root distribution angle; root diameter; landslide susceptibility mapping; random forest model; qualitative analysis; quantitative evaluation; Yunyang County; granular flow; ring-shear test; fluctuation characteristics; landslide; susceptibility; tree species; age group; woodland type; forest origin; YOLOv3; deep learning; automatic landslide identification; remote sensing image; flexible rockfall barrier; energy capacity; landslide debris; field investigation; coupled numerical simulation; landslide susceptibility; land-use suitability; random forest model; Hechuan District; landslide susceptibility assessment; deep learning; machine learning; Three Gorges Reservoir area; landslide; early-warning model; machine learning; Random Forest; model study; nucleation process; landslide; waveform similarity; repeating earthquakes; neighboring earthquakes; reinforcement of roots; slope stability; growth and decay; Japanese cedar trees; shallow landslides; n/a