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, S., & Jilani, M. (2026) 'Leveraging Graph Neural Networks for Explainable and Adaptive Seismic Risk Modelling (Work in Progress))', HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice, , pp. 118-118. Association for Computing Machinery (ACM): Available at: 10.1145/3777490.3779129