Bayesian probabilistic forecasting of rainfall-induced landslides

Forecasting landslides induced by rainfall is a challenging task that involves the interaction of multiple factors, such as soil conditions, topography, and rainfall intensity. The complex nature of these events, combined with the lack of complete data on landslide occurrences, makes it difficult to produce accurate predictions. Traditional deterministic models struggle to account for the variability and uncertainty inherent in the processes leading to landslides. On the contrary, probabilistic approaches can incorporate uncertainty and provide more reliable description of this phenomenon. In this work we develop a probabilistic framework for forecasting rainfall-induced landslide occurrence addressing the challenges of data sampling, uncertainty, and variability. For the study, we collected a dataset of shallow rainfall-induced landslides in an area in southern Italy, spanning 22 years of rainfall records. The dataset includes the locations and date of occurrences of the landslides, and daily rainfall measurements. Using a Bayesian approach, we calculate the posterior probability of landslide occurrence given specific daily cumulated rainfall thresholds. To account for the uncertainty in the landslide and rainfall data, we employed probabilistic distributions i.e., uniform and beta distributions, to model the uncertainty in the prior and likelihood functions. The uncertainty was further addressed through random sampling techniques, allowing for the integration of data variability and the dependencies between landslides and rainfall, obtaining posterior probability distributions of landslide occurrence for each rainfall threshold. The results offer a probabilistic approach to landslide forecasting that can be used for better-informed decision-making in risk management and early warning systems. By accounting for the uncertainties in the data and model parameters, our approach provides a more robust method for landslide prediction under varying rainfall conditions.