ORCID
- Musfira Jilani: 0000-0001-9632-957X
Abstract
Seismic risk modeling predicts the likelihood of earthquake occurrences in different regions over time. This presents a distinctive challenge as both the spatial structure of underlying geological features and the temporal evolution of seismic events must be accounted for accurately. In addition, for planners and decision-makers, it is essential that such models are not only accurate but also explainable. However, most existing models lack explainability, which limits their usefulness. This research proposes a novel graph neural network based seismic risk modeling methodology that is both explainable as well as adaptable, as it allows decision-makers the flexibility to choose the risk mapping region size as well as gain insights into the factors associated with a certain risk prediction.
Keywords
Explainability, Graph Convolutional Networks, Seismic modelling
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
118
Last Page
118
Deposit Date
2026-05-11
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Tyagi, Shivam and Jilani, Musfira, "Leveraging Graph Neural Networks for Explainable and Adaptive Seismic Risk Modelling (Work in Progress))" (2026). Research Outputs: 2025-Present. 15.
https://arrow.tudublin.ie/faccomentro/15