Physics-informed recurrent network reaches 81.2% validation ROC-AUC in Mount Umyeon case study

A physics-informed recurrent neural network achieved an 81.20% validation ROC-AUC for an annual peak-probability map of landslides in a Mount Umyeon case study in South Korea, slightly outperforming a conventional gated recurrent unit model. The method is designed for a specific data limitation: the study area had one year of daily rainfall observations, while its landslide inventory identified locations but did not contain the dates on which landslides occurred. By combining model-derived temporal information with rainfall sequences, terrain variables and mapped landslide locations, the researchers produced daily failure-probability estimates, a spatiotemporal risk representation and spatially varying rainfall thresholds. The result was a case-study performance measure rather than a demonstrated reduction in landslides or a general forecast guarantee for other regions.

The framework had three connected stages. First, the researchers addressed the missing event dates by constructing a physics-based estimate of daily probability of failure, called PoF-day. They generated multiple rainfall scenarios from the area’s one-year daily rainfall series through a Monte Carlo approach and passed each scenario through a hydrological model coupled with an infinite-slope model. The hydrological component represented rainfall effects, while the slope-stability component related those effects to terrain, allowing the procedure to generate a spatial and temporal distribution of daily failure likelihood. When evaluated against the complete landslide inventory, this physics-based PoF model attained a receiver-operating-characteristic area under the curve, or ROC-AUC, of 64%. These model estimates supply temporal information unavailable in the undated inventory, but the authors explicitly caution that the resulting soft labels cannot replace observed dates of landslide occurrence.

The second stage trained a recurrent neural network on sequences of daily rainfall. In addition to rainfall timing, the model incorporated static conditioning factors describing the landscape: elevation, aspect, curvature, terrain ruggedness index, stream power index, soil drainage characteristics, geology and forest cover. The inventory provided hard labels associated with landslide locations, while the PoF-day estimates provided soft labels carrying physics-based temporal information. From these inputs, the network estimated landslide probability for each day and generated an annual peak probability, or APP, map by taking the maximum daily probability within the year at each location. The APP map produced an ROC-AUC of 80.40% when assessed for spatial agreement with the training data and 81.20% on independent validation data. Its spatial discrimination was comparable to that obtained from an annual mean probability map and a map based on the 95th percentile of daily probability.

The physics-informed gated recurrent unit, abbreviated PI-GRU, also performed slightly better than the conventional GRU on the validation data. Its validation ROC-AUC was 0.8120, compared with 0.7943 for the conventional model, while its precision–recall area under the curve was 0.5003, compared with 0.4842. These differences represent a marginal predictive improvement rather than evidence of a major change in model performance. To explain how the landscape variables shaped the network’s estimates, the researchers used SHapley Additive exPlanations, or SHAP, and Local Interpretable Model-agnostic Explanations, or LIME. Both attribution approaches identified elevation, soil drainage characteristics and terrain ruggedness index as the most influential conditioning factors. The analyses therefore point to terrain and drainage-related variables as important inputs to the modeled landslide probabilities in the study landscape.

The final stage converted the probability estimates into graded warning levels. Researchers derived spatial rainfall thresholds from the APP map and daily predictions, then considered accumulated rainfall over one day, three days and seven days, represented by R1, R3 and R7 across the area of interest. These thresholds connect the model’s terrain-dependent probability estimates to alert levels based on specified rainfall accumulations. The available evidence does not establish how accurately the levels predict individual events, nor does it show that the framework prevents landslides or improves safety outcomes. The authors likewise identify the need for further testing with independent rainfall years and additional landslides whose occurrence dates are known. Accordingly, the reported performance supports the approach as a promising way to add temporal information to an undated landslide inventory, while its reliability beyond the Mount Umyeon case and under different rainfall and event conditions remains unestablished.

Subject of Research: Physics-informed recurrent modeling for probabilistic landslide early warning using rainfall, terrain and an undated landslide inventory

Article Title: Spatiotemporal probabilistic analysis for landslide early warning using physics-informed recurrent neural network model: case study Mt. Umyeon, Korea

Article References: Nguyen, B.-Q.-V., & Nguyen, T.-H. (2026). Spatiotemporal probabilistic analysis for landslide early warning using physics-informed recurrent neural network model: case study Mt. Umyeon, Korea. Natural Hazards, 122(22), Article 688. https://doi.org/10.1007/s11069-026-08478-y

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08478-y

Keywords: landslide early warning, physics-informed machine learning, recurrent neural network, Mount Umyeon, daily rainfall, probability of failure, spatiotemporal modeling, rainfall thresholds, ROC-AUC, South Korea, Spatiotemporal, probabilistic