From spatio-temporal landslide susceptibility to landslide risk forecast

Tengfei Wang, Ashok Dahal, Zhice Fang, Cees van Westen, Kunlong Yin*, Luigi Lombardo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
27 Downloads (Pure)


The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

Original languageEnglish
Article number101765
JournalGeoscience Frontiers
Issue number2
Publication statusPublished - Mar 2024


  • Dynamic landslide susceptibility
  • Future projections
  • Landslide risk
  • Space-time statistics


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