A Study of Landslide Susceptibility Assessment and Trend Prediction Using a Rule-Based Discrete Grid Model

Landslides are common natural disasters in mountainous regions, exerting considerable influence on socioeconomic development and city construction. Landslides occur and develop rapidly, often posing a significant threat to the safety of individuals and their property. Consequently, the mapping of areas susceptible to landslides and the simulation of the development of such events are crucial for the early warning and forecasting of regional landslide occurrences, as well as for the management of associated risks. In this study, a landslide susceptibility (LS) model was developed using an ensemble machine learning (ML) approach which integrates geological and geomorphological data, hydrological data, and remote sensing data. A total of nine factors (e.g., surface deformation rates (SDF), slope, and aspect) were used to assess the susceptibility of the study area to landslides and a grading of the LS in the study area was obtained. The proposed model demonstrates high accuracy and good applicability for LS. Additionally, a simulation of the landslide process and velocity was constructed based on the principles of landslide movement and the rule-based discrete grid model. Compared with actual unmanned aerial vehicle (UAV) imagery, this simulation model has a Sørensen coefficient (SC) of 0.878, a kappa coefficient of 0.891, and a total accuracy of 94.12%. The evaluation results indicate that the model aligns well with the spatial and temporal development characteristics of landslides, thereby providing a valuable reference basis for monitoring and early warning of landslide events.