Applicability of spatial prediction models for landslide susceptibility in land-use zoning instruments: a guideline in a coastal settlement in South-Central Chile

Abstract

Landslide hazard is usually incorporated into land-use planning as susceptibility zoning. Multiple-variable models have been widely used for susceptibility zoning due to their advantage to use different performance techniques to improve their prediction capacity. In Chile, the incorporation of landslide hazard into land-use planning instruments lacks a frame of reference that defines the susceptibility zoning and potential methodologies to be used. To overcome this lack of reference, this study assessed the applicability of Generalized Linear Model (GLM) and Generalized Additive Model (GAM) models in the establishment of susceptibility zoning guidelines. The application of both models identified the areas-prone to landslides in enough detail for a scale <1:15.000. The prediction of these zones was improved after spatial k-fold cross-validation, which indicated a slight advantage of the GAM model over the GLM model in susceptibility prediction capacity. The resulting zoning areas for low, moderate, high and very high susceptibility were very similar for the GLM and GAM models.

Keywords:

• Landslides
• spatial predictive models
• susceptibility zoning
• land-use planning

Acknowledgements

We would like to express our deep gratitude for the generous support of Dr. Alexander Brenning from the Friedrich-Schiller Universität Jena, Germany for offering answers and guidance about the spatial predictive models and providing some RStudio coding to carry out this study.

Disclosure statement

This work does not have funding with a potential conflict of interest by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Pablo López, upon reasonable request.

Additional information

Funding

This work was funded by the National Agency of Research and Development ANID (Chile)/National Master’s Scholarship/2016 – No. 22162015 and FONDECYT No. 11191011.