Ashok Dahal
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View article: Long and short-term perspectives on space–time landslide modelling
Long and short-term perspectives on space–time landslide modelling Open
View article: Pan‐European Landslide Risk Assessment: From Theory to Practice
Pan‐European Landslide Risk Assessment: From Theory to Practice Open
Assessing landslide risk is a fundamental requirement to plan suitable prevention actions. To date, most risk studies focus on individual slopes or catchments. Whereas regional, national or continental scale assessments are hardly availabl…
View article: First Pockmark susceptibility map of the Italian continental margins
First Pockmark susceptibility map of the Italian continental margins Open
View article: Spatial prediction of InSAR-derived hillslope velocities via deep learning
Spatial prediction of InSAR-derived hillslope velocities via deep learning Open
Spatiotemporal patterns of earth surface deformation are influenced by a combination of static and dynamic environmental characteristics specific to any landscape of interest. Nowadays, these patterns can be captured for larger areas using…
View article: Towards physics-informed neural networks for landslide prediction
Towards physics-informed neural networks for landslide prediction Open
View article: Quantifying the influence of topographic amplification on the landslides triggered by the 2015 Gorkha earthquake
Quantifying the influence of topographic amplification on the landslides triggered by the 2015 Gorkha earthquake Open
Topographic amplification is caused by the interaction between seismic waves and rough terrains. It increases shaking levels on hilltops and could lead stable slopes to the brink of failure. However, its contribution to coseismic landslide…
View article: Distribution-agnostic landslide hazard modelling via Graph Transformers
Distribution-agnostic landslide hazard modelling via Graph Transformers Open
View article: At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition
At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition Open
The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated whe…
View article: An ensemble neural network approach for space–time landslide predictive modelling
An ensemble neural network approach for space–time landslide predictive modelling Open
There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on temporally-aggregated measures of rainfall derived from either in-situ measurements or satellite-based rainfall est…
View article: Towards physics-informed neural networks for landslide prediction
Towards physics-informed neural networks for landslide prediction Open
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 success and spread of such tools came from the ability to ex…
View article: Comprehensive review of pockmarks and first "Susceptibility Map" of the Italian Continental Margins
Comprehensive review of pockmarks and first "Susceptibility Map" of the Italian Continental Margins Open
Fluids, encompassing gases and liquids, possess lesser density than solids, therefore exhibit an upward movement within sedimentary strata due to buoyancy. Seafloor "fluid flow" is a well-established phenomenon in diverse geodynamic settin…
View article: Space–time landslide hazard modeling via Ensemble Neural Networks
Space–time landslide hazard modeling via Ensemble Neural Networks Open
Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physically based models. The part of the geoscientific community in developing data-driven models has instead focused on pred…
View article: Dynamic Susceptibility of Rainfall-Induced Landslides: A Gated Recurrent Unit Approach
Dynamic Susceptibility of Rainfall-Induced Landslides: A Gated Recurrent Unit Approach Open
Globally, there is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single aggregated measure of rainfall derived from either in-situ measurements or radar estimates. Rely…
View article: Assessing landslide risk on a Pan-European scale
Assessing landslide risk on a Pan-European scale Open
Assessing landslide risk is a fundamental step in planning prevention and mitigation actions in mountainous landscapes. To date, most landslide risk analyses address this topic at the scale of a slope or catchment. Whenever the scale invol…
View article: An ensemble neural network approach for space-time landslide predictive modelling
An ensemble neural network approach for space-time landslide predictive modelling Open
There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single temporally-aggregated measure of rainfall derived from either in-situ measurements or satellite-based rain…
View article: Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction
Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction Open
Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. S…
View article: On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values
On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values Open
Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are globally occurring natural hazards which pose great threats to our society, leading to fatalities a…
View article: Data for "At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition"
Data for "At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition" Open
View article: Data for "At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition"
Data for "At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition" Open
View article: At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition Open
The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated whe…
View article: Deep graphical regression for jointly moderate and extreme Australian wildfires
Deep graphical regression for jointly moderate and extreme Australian wildfires Open
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme w…
View article: Data for the paper "Transformers, more than meets the eye: employing full waveforms for coseismic landslide prediction"
Data for the paper "Transformers, more than meets the eye: employing full waveforms for coseismic landslide prediction" Open
View article: Data for the paper "Transformers, more than meets the eye: employing full waveforms for coseismic landslide prediction"
Data for the paper "Transformers, more than meets the eye: employing full waveforms for coseismic landslide prediction" Open
View article: A Closer Look into Variables Controlling Hillslope Deformations in the Three Gorges Reservoir Area
A Closer Look into Variables Controlling Hillslope Deformations in the Three Gorges Reservoir Area Open
View article: Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system
Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system Open
The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven proba- bilistic solut…
View article: Investigating earthquake legacy effect on hillslope deformation using InSAR‐derived time series
Investigating earthquake legacy effect on hillslope deformation using InSAR‐derived time series Open
Mountainous landscapes affected by strong earthquakes typically exhibit higher landslide susceptibility in post‐seismic periods compared to pre‐seismic conditions. This concept is referred to as the earthquake legacy effect, which needs to…
View article: From spatio-temporal landslide susceptibility to landslide risk forecast
From spatio-temporal landslide susceptibility to landslide risk forecast Open
The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This…
View article: Speech-recognition in landslide predictive modelling: A case for a next generation early warning system
Speech-recognition in landslide predictive modelling: A case for a next generation early warning system Open
Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling…
View article: From ground motion simulations to landslide occurrence prediction
From ground motion simulations to landslide occurrence prediction Open
View article: Deep graphical regression for jointly moderate and extreme Australian wildfires
Deep graphical regression for jointly moderate and extreme Australian wildfires Open
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme w…