Bayesian forecasting of triggered landslides

We present a Bayesian probabilistic framework for landslide forecasting, explicitly accounting for the sources of epistemic uncertainty that affect landslide occurrence. The method describes the probability of landslide occurrence as a distribution, rather than a single value, allowing a more realistic treatment of uncertainty arising from incomplete landslide inventories, variable measurements, and the inherent complexity of landslide processes. We apply the probabilistic framework to a 22-year dataset of shallow landslides and daily rainfall records from the Campania region (southern Italy). Each landslide is associated with the nearest rain gauge, and forecasts are computed within Thiessen polygons representing the area of influence of each rain gauge. Posterior landslide probabilities are calculated for different daily rainfall thresholds using Bayes' theorem, with prior and likelihood terms modelled as uniform and Beta distributions, respectively. Results show that posterior probabilities increase progressively with rainfall, ranging from 0 to 10 −3 at the lowest threshold (0.2 mm) to up to 10 −1 at the highest threshold (70 mm) when uncertainty is not considered, and from 10 −3 to values between 0.2 and 0.8 at the highest threshold (70 mm) when uncertainty is accounted for, and no sharp physical threshold emerges. The retrospective forecast skill improves with rainfall information, as demonstrated by consistent gains in posterior over prior probabilities. This gradual increase in probability, without the emergence of a sharp physical threshold, supports the use of probabilistic rather than deterministic approaches for landslide forecasting in operational contexts. The proposed Bayesian probabilistic framework is designed to be generalizable to other triggering mechanism (e.g., earthquakes) and potentially adaptable to other regions, provided that sufficient data are available. Although the method is data-intensive, it enables transparent, uncertainty-informed forecasts, with potential applications in early warning systems and risk management strategies. Future developments may include the incorporation of antecedent rainfall and geological conditioning factors across broader spatial and temporal scales.