Expertise
Engineering & Materials Science
# Deep Learning
# Disasters
# Geographic Information Systems
# Landslides
# Population Distribution
# Satellite Imagery
Earth & Environmental Sciences
# Landslide
# Population Distribution
Faculty of Geo-Information Science and Earth Observation (ITC), Department of Applied Earth Sciences (AES)Research
Geo-AI Based Modelling of Earthquake Induced Landslides Using Ground Motion Simulation
Publications
Recent
Elia, L., Castellaro, S.
, Dahal, A.
, & Lombardo, L. (2023).
Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example.
Science of the total environment,
898, Article 165289. Advance online publication.
https://doi.org/10.1016/j.scitotenv.2023.165289
Cisneros, D., Richards, J.
, Dahal, A.
, Lombardo, L., & Huser, R. (2023).
Deep graphical regression for jointly moderate and extreme Australian wildfires. ArXiv.org.
https://doi.org/10.48550/arXiv.2308.14547
Ahmed, M.
, Tanyas, H., Huser, R.
, Dahal, A., Titti, G., Borgatti, L., Francioni, M.
, & Lombardo, L. (2023).
Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system. Earth ArXiv.
https://doi.org/10.31223/X5JT2D
Elia, L., Castellaro, S.
, Dahal, A.
, & Lombardo, L. (2023).
Assessing multi-hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example. Earth ArXiv.
https://doi.org/10.31223/X5RQ27
Wang, T.
, Dahal, A., Fang, Z.
, van Westen, C., Yin, K.
, & Lombardo, L. (2023).
From space-time landslide susceptibility to landslide risk forecast. Earth ArXiv.
https://doi.org/10.31223/X5XT1F
Mishra, B., Bhandari, R., Bhandari, K. P., Bhandari, D. M., Luintel, N.
, Dahal, A., & Poudel, S. (2023).
High-resolution mapping of seasonal crop pattern using sentinel imagery in mountainous region of Nepal: A semi-automatic approach.
Geomatics,
3(2), 312-327.
https://doi.org/10.3390/geomatics3020017
Dahal, A.
, & Lombardo, L. (2023).
Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling.
Computers & geosciences,
176, Article 105364. Advance online publication.
https://doi.org/10.1016/j.cageo.2023.105364
Wang, N., Zhang, H.
, Dahal, A., Cheng, W., Zhao, M.
, & Lombardo, L. (2023).
On the use of explainable AI for susceptibility modeling: examining the spatial pattern of SHAP values. Earth ArXiv.
https://doi.org/10.31223/X5P078
Tanyas, H., He, K., Sadhasivam, N.
, Lombardo, L.
, Chang, L., Fang, Z.
, Dahal, A.
, Fadel, I., Hu, X., & Luo, G. (2023).
Monitoring and prediction of InSAR-derived post-seismic hillslope deformation rates. Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-14415
Dahal, A.
, Tanyas, H.
, van Westen, C.
, van der Meijde, M., Mai, P. M., Huser, R.
, & Lombardo, L. (2023).
Space-time modelling of co-seismic and post-seismic landslide hazard via Ensemble Neural Networks.. Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-3496
UT Research Information System
Google Scholar Link
Contact Details
Visiting Address
University of Twente
Faculty of Geo-Information Science and Earth Observation
Langezijds
(building no. 19), room 2419
Hallenweg 8
7522NH Enschede
The Netherlands
Mailing Address
University of Twente
Faculty of Geo-Information Science and Earth Observation
Langezijds
2419
P.O. Box 217
7500 AE Enschede
The Netherlands