Modeling the size of co-seismic landslides via data-driven models: the Kaikōura's example

Moreno, Mateo; Steger, Stefan; Tanyaş, Hakan; Lombardo, Luigi

doi:10.31223/x5vd1p

Cited by 5 publications

(5 citation statements)

References 62 publications

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“…So far, no spatially nor temporally explicit model exists for landslide area density. However, four recent articles have explored the capacity of predicting landslide areas (Lombardo et al, 2021;Aguilera et al, 2022;Bryce et al, 2022;Moreno et al, 2022). All of them have returned suitable predictive performance, but still far from the match seen in the second panel of Figure 7, between observed and predicted landslide density.…”

Section: Resultsmentioning

confidence: 99%

Space-time landslide hazard modeling via Ensemble Neural Networks

Dahal¹,

Tanyaş²,

Westen³

et al. 2022

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

Space-time landslide hazard modeling via Ensemble Neural Networks

Dahal¹,

Tanyaş²,

Westen³

et al. 2022

Preprint

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“…Therefore, the point we are trying to raise here is questioning whether the susceptibility framework shouldn't be considered largely solved (Ozturk et al 2021), whenever heavily non-linear models are tasked with distinguishing the distribution of landslides purely in space. Conversely, the data-driven estimation of landslide intensity (Lari et al, 2014), whether it is spatially (Moreno et al, 2023), temporally (Nava et al, 2023), or spatiotemporally (Fang et al, 2024) addressed it is still at an infancy stage where few contributions are available and much may still be gained from a common geoscientific effort.…”

Section: Binary Vs Count Based Modelling Reflectionsmentioning

confidence: 99%

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

Bryce,

Castro-Camilo,

Dashwood

et al. 2024

Preprint

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At the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britian. The ability to use a data-driven methodology to develop a national scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of the spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

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“…For this reason, we already envision future models that would take the measured extent of TSs and TEGs as the response variable, this time solving a regression task rather than a classification one as per the susceptibility requirement. Such a direction has recently been explored for landslides occurring at lower latitudes (Lombardo et al, 2021;Moreno et al, 2022). An even better extension has already been tested in which the expectation of locations prone to landslides are modelled, together with the expectation of the resulting landslide size (Aguilera et al, 2022;Bryce et al, 2022).…”

Section: Considerations Within and Beyond Svalbard: Supporting And Op...mentioning

confidence: 99%

Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago

Nicu

Elia

Rubensdotter

et al. 2023

Earth Syst. Sci. Data

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Abstract. The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TSs) and thermo-erosion gullies (TEGs) could take place and rapidly develop under the influence of climatic variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming, and therefore, a deeper understanding of TSs and TEGs is necessary to understand and foresee the dynamics behind local cryospheric hazards' occurrences and their global implications. We present the latest update of two polygonal inventories where the extent of TSs and TEGs is recorded across Nordenskiöld Land (Svalbard Archipelago), over a surface of approximately 4000 km2. This area was chosen because it represents the most concentrated ice-free area of the Svalbard Archipelago and, at the same time, where most of the current human settlements are concentrated. The inventories were created through the visual interpretation of high-resolution aerial photographs as part of our ongoing effort toward creating a pan-Arctic repository of TSs and TEGs. Overall, we mapped 562 TSs and 908 TEGs, from which we separately generated two susceptibility maps using a generalised additive model (GAM) approach, under the assumption that TSs and TEGs manifest across Nordenskiöld Land, according to a Bernoulli probability distribution. Once the modelling results were validated, the two susceptibility patterns were combined into the first multi-hazard cryospheric susceptibility map of the area. The two inventories are available at https://doi.org/10.1594/PANGAEA.945348 (Nicu et al., 2022a) and https://doi.org/10.1594/PANGAEA.945395 (Nicu et al., 2022b).

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Modeling the size of co-seismic landslides via data-driven models: the Kaikōura's example

Cited by 5 publications

References 62 publications

Space-time landslide hazard modeling via Ensemble Neural Networks

Space-time landslide hazard modeling via Ensemble Neural Networks

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago

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