80 citations · 99 across the 8 of their papers we have counts for
9 papers
Towards physics-informed neural networks for landslide prediction
Ashok Dahal, Luigi Lombardo
For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, disconnected from the physics of the failure mechanism. The s…
Statistics of extremes for natural hazards: landslides and earthquakes
Rishikesh Yadav, Luigi Lombardo, Raphaël Huser
In this chapter, we illustrate the use of split bulk-tail models and subasymptotic models motivated by extreme-value theory in the context of hazard assessment for earthquake-induc…
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
Ashok Dahal, Raphaël Huser, Luigi Lombardo
The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the…
Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions
Rishikesh Yadav, Raphaël Huser, Thomas Opitz +1
To accurately quantify landslide hazard in a region of Turkey, we develop new marked point process models within a Bayesian hierarchical framework for the joint prediction of lands…
High-resolution Bayesian mapping of landslide hazard with unobserved trigger event
Thomas Opitz, Haakon Bakka, Raphaël Huser +1
Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution.…
Space-Time Landslide Predictive Modelling
Luigi Lombardo, Thomas Opitz, Francesca Ardizzone +2
Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment. Investigators have for long attempted to estimate landslide hazard to…