Landslides are notoriously difficult to predict. Deep neural networks (DNNs)
models are more accurate than statistical models. However, they are
uninterpretable, making it difficult to extract mechanistic information about
landslide controls in the modeled region. We developed an explainable AI (XAI)
model to assess landslide susceptibility that is computationally simple and
features high accuracy. We validated it on three different regions of eastern
Himalaya that are highly susceptible to landslides. SNNs are computationally
much simpler than DNNs, yet achieve similar performance while offering insights
regarding the relative importance of landslide control factors in each region.
Our analysis highlighted the importance of: 1) the product of slope and
precipitation rate and 2) topographic aspects that contribute to high
susceptibility in landslide areas. These identified controls suggest that
strong slope-climate couplings, along with microclimates, play more dominant
roles in eastern Himalayan landslides. The model outperforms physically-based
stability and statistical models.Comment: 47 pages (including SI section); 3 main figures; 14 supplementary
figures; 9 supplementary table