Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.jag.2023.103593. This is version 2 of this Preprint.

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Authors

Mahnoor Ahmed, Hakan Tanyas , Raphaël Huser , Ashok Dahal , Giacomo Titti, Lisa Borgatti, Mirko Francioni, Luigi Lombardo 

Abstract

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions.
This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.

DOI

https://doi.org/10.31223/X5JT2D

Subjects

Geomorphology, Statistical Models

Keywords

dynamic susceptibility, landslide prediction, Early warning system, generalized additive models

Dates

Published: 2023-08-03 05:33

Last Updated: 2023-12-14 10:19

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None