Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica

Abstract

Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially the case in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration of rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.

Space-time landslide hazard modeling via Ensemble Neural Networks

“…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.…”

“…In their work, the authors exclusively estimated the potential landslide size at a given location, without informing whether the given location would have been susceptible in the first place. This limitation has been further addressed by Bryce et al (2022) and Aguilera et al (2022), implementing models that couple susceptibility and landslide area prediction together. Nevertheless, even in these cases, the absence of the temporal dimension in their work implies that no current datadriven model has even been capable to solve the landslide hazard definition (Guzzetti et al, 1999), jointly estimating where, when (or how frequently) and how large landslides may be in a given spatio-temporal domain.…”

Space-time landslide susceptibility modelling in Taiwan

“…This is something that has already been observed in other susceptibility studies , and it is usually interpreted under the assumption that upwardly concave morphologies would experience acceleration in terms of overland flows and thus lead to higher erosion and destabilization capacity (Ohlmacher, 2007). With regards to the slope exposition, we opted for a generally accepted strategy where the terrain aspect is decomposed into northness and eastness (Cama et al, 2017;Lombardo et al, 2020a;Samia et al, 2020;Bryce et al, 2022). Among the available contributions in Tawain, Lee (2013) indicated that slopes facing south and southeast hosted more frequently landslides than others during the Chi-Chi earthquake.…”

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

“…In mountainous regions, natural hazards such as landslides, avalanches, floods, and debris flows can cause significant property damage and human casualties 1 . Landslides are triggered by earthquakes 2 – 4 , extreme meteorological events such as intense precipitation 5 and windstorms 6 – 9 , and anthropogenic activities 10 . Landslides alone have contributed to roughly $4.5 billion of economic loss between 1990 and 2017 according to the EM-DAT 11 , and approximately 58% of the fatalities and 69% of economic losses transpired in East and Southeast Asia, respectively.…”