Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Moreno, Mateo; Lombardo, Luigi; Crespi, Alice; Zellner, Peter James; Mair, Volkmar; Pittore, Massimiliano; van Westen, Cees; Steger, Stefan

doi:10.1016/j.scitotenv.2023.169166

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2023.169166

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Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Mateo Moreno,

Luigi Lombardo,

Alice Crespi

et al.

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Cited by 9 publications

References 69 publications

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An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

Bryce,

Castro-Camilo,

Dashwood

et al. 2024

Landslides

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At the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

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An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

Bryce,

Castro-Camilo,

Dashwood

et al. 2024

Landslides

View full text Add to dashboard Cite

show abstract

Improved landslide prediction by considering continuous and discrete spatial dependency

Fang,

Wang,

Wang

et al. 2024

Landslides

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Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador)

Soto,

Galve,

Palenzuela

et al. 2024

Environ Earth Sci

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Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Cited by 9 publications

References 69 publications

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

Improved landslide prediction by considering continuous and discrete spatial dependency

Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador)

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