Physically-based catchment-scale prediction of slope failure volume and geometry

Bout, Bastian van den; Lombardo, Luigi; Ma, Chiyang; Westen, C.J. van; Jetten, Victor

doi:10.1016/j.enggeo.2020.105942

Cited by 26 publications

(14 citation statements)

References 77 publications

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“…They have sought to estimate where landslides may occur, being blind though to how large landslides may become, once they trigger in a given location. Conversely, this problem is typically addressed via physically-based approaches (Li et al 2012;Bout et al 2018; Van den Bout et al 2021), although their applications over large regions is commonly hindered by geotechnical data availability. On the contrary, data-driven approaches can by-pass this issue by using proxies instead of geotechnical parameters.…”

Section: Introductionmentioning

confidence: 99%

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

Aguilera

Lombardo

Tanyaş

et al. 2022

Stoch Environ Res Risk Assess

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For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.

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Section: Introductionmentioning

confidence: 99%

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

Aguilera

Lombardo

Tanyaş

et al. 2022

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this work we consider debris flows, and we develop spatial predictive models for a particularly severe event in 2009 during which multiple landslides were triggered simultaneously by heavy rainfall in a region of the island of Sicily, Italy. Debris flows are phenomena that consist of a mixed mass of water, soil and fragmented rocks which: (i) flows over mountainsides, (ii) funnels into channels, (iii) entrains objects while propagating downhill and (iv) lays waste through its path before (v) stopping over valley floors (van den Bout et al (2021)). As debris flows are primarily triggered by precipitation, rain discharge is positively correlated with landslide frequency.…”

Section: Introductionmentioning

confidence: 99%

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

et al. 2022

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Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive baseline model that captures the unobserved precipitation trigger through a spatial random effect at slope unit resolution, we explore novel complex model structures that take clusters of events arising at small spatial scales into account as well as nonlinear or spatially-varying covariate effects. For a 2009 event of around 5000 precipitation-triggered landslides in Sicily, Italy, we show how to fit our proposed models efficiently, using the integrated nested Laplace approximation (INLA), and rigorously compare the performance of our models both from a statistical and applied perspective. In this context we argue that model comparison should not be based on a single criterion and that different models of various complexity may provide insights into complementary aspects of the same applied problem. In our application our models are found to have mostly the same spatial predictive performance, implying that key to successful prediction is the inclusion of a slope-unit resolved random effect capturing the precipitation trigger. Interestingly, a parsimonious formulation of space-varying slope effects reflects a physical interpretation of the precipitation trigger: in subareas with weak trigger, the slope steepness is shown to be mostly irrelevant.

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“…They have sought to estimate where landslides may occur, being blind though to how large landslides may become, once they trigger in a given location. Conversely, this problem is typically addressed via physicallybased approaches (Li et al, 2012;Bout et al, 2018;Van den Bout et al, 2021), although their applications over large regions is commonly hindered by geotechnical data availability.…”

Section: Introductionmentioning

confidence: 99%

On The Prediction of Landslide Occurrences and Sizesvia Hierarchical Neural Networks

Aguilera

Lombardo

Tanyaş

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

For more than three decades, the scientific community that studies landslides through data-driven models has focused on estimating where landslides occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Network (NN). But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides in 2014. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in the supplementary material in the hope that we would stimulate others to replicate similar analyses.

show abstract

Physically-based catchment-scale prediction of slope failure volume and geometry

Cited by 26 publications

References 77 publications

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

On The Prediction of Landslide Occurrences and Sizesvia Hierarchical Neural Networks

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