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

Bryce, Erin; Castro-Camilo, Daniela; Dashwood, Claire; Tanyas, Hakan; Ciurean, Roxana; Novellino, Alessandro; Lombardo, Luigi. 2024 An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland. Landslides. https://doi.org/10.1007/s10346-024-02368-9

Before downloading, please read NORA policies.

Preview

Text (Open Access Paper) s10346-024-02368-9.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (15MB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s10346-024-02368-9
ISSN:	1612-510X
Date made live:	22 Nov 2024 13:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538424

Actions (login required)

View Item

Document Downloads

More statistics for this item...