Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Steger, Stefan; Moreno, Mateo; Crespi, Alice; Luigi Gariano, Stefano; Teresa Brunetti, Maria; Melillo, Massimo; Peruccacci, Silvia; Marra, Francesco; de Vugt, Lotte; Zieher, Thomas; Rutzinger, Martin; Mair, Volkmar; Pittore, Massimiliano

doi:10.1016/j.gsf.2024.101822

Geoscience Frontiers

2024

DOI: 10.1016/j.gsf.2024.101822

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Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Stefan Steger,

Mateo Moreno,

Alice Crespi

et al.

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Cited by 3 publications

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Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

Di Napoli,

Eroglu,

van den Bout

et al. 2024

CATENA

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Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

Di Napoli,

Eroglu,

van den Bout

et al. 2024

CATENA

View full text Add to dashboard Cite

Towards physics-informed neural networks for landslide prediction

Dahal,

Lombardo

2025

Engineering Geology

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High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping

Peiro,

Volpe,

Ciabatta

et al. 2024

Geosciences

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Satellite-derived high-resolution soil moisture and precipitation data have become widely adopted in natural hazard and climate change research. Landslide susceptibility mapping, which often relies on static predisposing factors, faces challenges in accounting for temporal changes, limiting its efficacy in accurately identifying potential locations for landslide occurrences. A key challenge is the lack of sufficient ground-based monitoring networks for soil moisture and precipitation, especially in remote areas with limited access to rain gauge data. This study addresses these limitations by integrating static landslide conditioning factors—such as topography, geology, and landscape features—with high-resolution dynamic satellite data, including soil moisture and precipitation. Using machine learning techniques, particularly the random forest (RF) algorithm, the approach enables the generation of dynamic landslide susceptibility maps that incorporate both spatial and temporal variations. To validate the proposed method, two significant rainfall events that occurred in Italy in October and November 2019—each triggering more than 40 landslides—were analyzed. High-resolution satellite rainfall and soil moisture data were integrated with statistical conditioning factors to identify high-probability landslide areas successfully. A differential susceptibility map was generated for these events to compare the results between them, illustrating how susceptibility variations within the study area are influenced by hydrological factors. The distinct susceptibility patterns associated with different hydrological conditions were accurately captured. It is suggested that future research focus on leveraging time-series high-resolution satellite data to enhance landslide susceptibility assessments further.

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Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Cited by 3 publications

References 80 publications

Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

Towards physics-informed neural networks for landslide prediction

High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping

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