Space-time landslide hazard modeling via Ensemble Neural Networks

Abstract

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published research have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size, has hardly ever been modeled over space and time. However, the technological advancements of data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size) mentioned above. 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 ambitious task, we have used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake on the 25th of April 2015. 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 x 1 km and classified/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 depending on the mapped inventory dates. The results are promising as our model performs satisfactorily both in the classification (susceptibility) and regression (density prediction) tasks. We believe that the model we propose brings a level of novelty that has the potential to create a rift with respect to the common susceptibility literature, finally proposing an integrated framework for hazard modeling in a data-driven context.

Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data

“…According to the United States Geological Survey (USGS), the main shock generated strong ground shaking reaching up to a maximum Peak Ground Acceleration (PGA) of 0.87 g 61 . Various studies examined the spatial distribution of co-and/or post-seismic landslides 35,62,63 as well as their evolution over time via MT landslide inventories 36,56,64,65 . Here, we only focus on a subset of the area affected by the co-seismic landslide event (Fig.…”

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

“…Integrating space and time in data-driven modeling techniques is rarely done (Bajni et al, 2023;Tanyas, 2020, 2021). Nonetheless, there have been notable endeavors to incorporate dynamic controls into the modeling process by aggregating meteorological factors over specific periods (e.g., mean annual rainfall; maximum daily rainfall per inventoried period; Dahal et al, 2022;Wang et al, 2022). These approaches better capture long-term meteorological trends or predisposition rather than short-term dynamics.…”