AI-based simulation surrogates for planning rainfall-induced landslide mitigation

Landslides triggered by intense or prolonged rainfall are a growing threat in the context of climate change. Physically based models are commonly used to estimate slope stability under varying hydrogeological conditions. However, their high computational cost may limit their use in time-sensitive risk assessment and decision-making scenarios. We propose a novel application of artificial intelligence (AI) for planning in safety-critical domains. In particular, our approach accelerates the prediction of key slope stability indicators—the factor of safety, the depth of the sliding surface, and the final position of the water table—under various rainfall events. Using a large dataset of more than 16,000 simulations, we train regression models that quickly approximate the results of complex numerical analyses across various slope geometries, soil properties, and hydrological conditions. The proposed models can perform rapid risk assessment for rainfall-induced landslides, exploring various rainfall scenarios and slope responses in tight timeframes. This helps select mitigation strategies that prioritize safety while considering reliability, feasibility constraints, and the likelihood of rainfall events. Our approach is thus key for complex, real-world AI planning in hydrogeological risk management, in particular considering the context of climate change, which will increase the number of sites requiring effective protective measures and rapid intervention in years to come.