An explainable AI (XAI) model for landslide susceptibility modeling

Landslides are among the most devastating natural hazards, severely impacting human lives and damaging property and infrastructure. Landslide susceptibility maps, which help to identify which regions in a given area are at greater risk of a landslide occurring, are a key tool for effective mitigation. Research in this field has grown immensely, ranging from quantitative to deterministic approaches, with a recent surge in machine learning (ML)-based computational models. The development of ML models, in particular, has undergone a meteoritic rise in the last decade, contributing to the successful development of accurate susceptibility maps. However, despite their success, these models are rarely used by…

• UO
  University of Technology Sydney
• MO
  Ministry of Science, ICT and Future Planning
• KI
  Korea Institute of Geoscience and Mineral Resources
• NR
  National Research Foundation of Korea
• MO
  Ministry of Science and ICT, South Korea

  Award: 2023R1A2C1003095