An interpretable and transferable model for shallow landslides detachment combining spatial Poisson point processes and generalized additive models - Giulia Patanè; Teresa Bortolotti; Vasil Yordanov; Ludovico Giorgio Aldo Biagi; Maria Antonia Brovelli; Xuan Quang Truong; Simone Vantini (Stochastic Environmental Research and Risk Assessment, 2025)
Shallow landslides are rapidly moving and strongly dangerous slides. In the present work, the probabilistic distribution of the landslide detachment points within a valley is modelled as a spatial Poisson point process, whose intensity depends on geophysical covariates according to a generalized additive model. This jointly allows to obtain good predictive performance and to preserve the interpretability of the effects of the geophysical predictors on the intensity of the process. We propose a novel workflow, based on Random Forests, to select the geophysical predictors entering the model for the intensity. The statistically significant effects are interpreted as activating or stabilizing factors for landslide detachment. The transferability of the resulting model is guaranteed by training, validating and testing the algorithms on mutually disjoint valleys in the Alps of Lombardy (Italy). Finally, the uncertainty around the estimated intensity of the process is quantified via semiparametric bootstrap.

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards - Mara S. Bernardi; Pasquale C. Africa; Carlo de Falco; Luca Formaggia; Alessandra Menafoglio; Simone Vantini (Mathematical Geosciences, 2021)
Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensor images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data collected nowadays and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis.

Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction - Konrad Abramowicz; Per Arnqvist; Piercesare Secchi; Sara Sjöstedt de Luna; Simone Vantini; Valeria Vitelli (Stochastic Environmental Research and Risk Assessment, 2017)

An Integrated Statistical Investigation of Internal Carotid Arteries of Patients Affected by Cerebral Aneurysms - Tiziano Passerini; Laura M. Sangalli; Simone Vantini; Marina Piccinelli; Susanna Bacigaluppi; Luca Antiga; Edoardo Boccardi; Piercesare Secchi; Alessandro Veneziani (Cardiovascular Engineering and Technology, 2012)