Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China

Landslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides' differential characteristics in different sub-regions. Based on data of lithology, elevation, and historical landslides, terrain units (TUs) and slope units (SUs) were obtained. LSM was developed using the Random Forest (RF) model and Light Gradient Boosting Machine (LGBM) model. The LGBM-TUs showed the highest performance and were therefore, selected to obtain LSM. The study area was divided into four sub-regions using the geographically weighted regression (GWR) model, along with spatial differential characteristics of topography conditions. The distribution and characteristics of landslides within each sub-region were assessed using GeoDetector. The results illustrated the reliability of the LGBM-TUs model. Lithology, elevation, and average annual rainfall were the dominant factors, while the influence of other factors on the occurrence of landslides was strengthened only when these factors interacted. This study proposed a new method for LSM research to insight the spatial differential characteristics of landslides in various sub-regions. Our results provide novel insights into landslide mitigation.

Automated summary

This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides' differential characteristics in different sub-regions. The LGBM-TUs model showed the highest performance and lithology, elevation, and average annual rainfall were the dominant factors. The results provide novel insights into landslide mitigation and propose a new method for understanding the spatial differential characteristics of landslides in various sub-regions.