On the estimation of landslide intensity, hazard and density via data-driven models

Napoli, Mariano Di; Tanyaş, Hakan; Castro‐Camilo, Daniela; Calcaterra, Domenico; Cevasco, Andrea; Martire, Diego Di; Pepe, Giacomo; Brandolini, Pierluigi; Lombardo, Luigi

doi:10.31223/x52d1j

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…Such models can obviously provide much more information than modeling only susceptibility (Lombardo et al, 2021). Only using the susceptibility information is blind to landslide characteristics, such as how many landslides may manifest or how large they may become once they start moving downhill (Di Napoli et al, 2023). Thus, the combined information of which slope may be considered unstable and the expectation on the landslide can become an important source of information, not only for hazard assessment but even for risk reduction and management practitioners, once combined with the elements at risk.…”

Section: Supporting Argumentsmentioning

confidence: 99%

Space–time landslide hazard modeling via Ensemble Neural Networks

Dahal,

Tanyas,

van Westen

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physically based models. The part of the geoscientific community in developing data-driven models has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimate when landslides may occur via models that belong to the early-warning system or to the rainfall-threshold classes. In this context, few published research works have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size (i.e., areas or volumes), has hardly ever been modeled over space and time. However, technological advancements in data-driven models have reached a level of maturity that allows all three components to be modeled (Location, Frequency, and Size). This work takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this, we used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 km × 1 km and classified or regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6 month resolution. The results are promising as our model performs satisfactorily both in the susceptibility (AUC = 0.93) and density prediction (Pearson r = 0.93) tasks over the entire spatio-temporal domain. This model takes a significant distance from the common landslide susceptibility modeling literature, proposing an integrated framework for hazard modeling in a data-driven context.

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Section: Supporting Argumentsmentioning

confidence: 99%