Ashok Dahal

University of Twente

Netherlands

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Articles (20)

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2025

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2023

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2022

Collaborators (4)

Daniele Spatola

Sapienza University of Rome

ITALY

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Luigi Lombardo

University of Twente

NETHERLANDS

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Hakan Tanyas

Assistant Professor

University of Twente

NETHERLANDS

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Anton Vrieling

Associate Professor

University of Twente

NETHERLANDS

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Ashok Dahal

Research Interests

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Articles (20)

First Pockmark susceptibility map of the Italian continental margins

Pan‐European Landslide Risk Assessment: From Theory to Practice

Spatial prediction of InSAR-derived hillslope velocities via deep learning

Distribution-agnostic landslide hazard modelling via Graph Transformers

Towards physics-informed neural networks for landslide prediction

Space–time landslide hazard modeling via Ensemble Neural Networks

Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction

At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition

An ensemble neural network approach for space–time landslide predictive modelling

On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values

Deep graphical regression for jointly moderate and extreme Australian wildfires

From spatio-temporal landslide susceptibility to landslide risk forecast

Quantifying the influence of topographic amplification on the landslides triggered by the 2015 Gorkha earthquake

A closer look into variables controlling hillslope deformations in the Three Gorges Reservoir Area

Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling

High-resolution mapping of seasonal crop pattern using sentinel imagery in mountainous region of Nepal

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

Speech-recognition in landslide predictive modelling

From ground motion simulations to landslide occurrence prediction

Methods in the spatial deep learning: current status and future direction

Collaborators (4)

Daniele Spatola

Luigi Lombardo

Hakan Tanyas

Anton Vrieling

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