Impacts from cascading multi-hazards using hypergraphs: a case study from the 2015 Gorkha earthquake in Nepal
<p>This study introduces a new approach to multi-hazard risk assessment, leveraging hypergraph theory to model the interconnected risks posed by cascading natural hazards. Traditional single-hazard risk models fail to account for the complex interrelationships and compounding effects of multip...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/267/2025/nhess-25-267-2025.pdf |
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| Summary: | <p>This study introduces a new approach to multi-hazard risk assessment, leveraging hypergraph theory to model the interconnected risks posed by cascading natural hazards. Traditional single-hazard risk models fail to account for the complex interrelationships and compounding effects of multiple simultaneous or sequential hazards. By conceptualising risks within a hypergraph framework, our model overcomes these limitations, enabling efficient simulation of multi-hazard interactions and their impacts on infrastructure. We apply this model to the 2015 <span class="inline-formula"><i>M</i><sub>w</sub></span> 7.8 Gorkha earthquake in Nepal as a case study, demonstrating its ability to simulate the primary and secondary effects of the earthquake on buildings and roads across the whole earthquake-affected area. The model predicts the overall pattern of earthquake-induced building damage and landslide impacts, albeit with a tendency towards over-prediction. Our findings underscore the potential of the hypergraph approach for multi-hazard risk assessment, offering advances in rapid computation and scenario exploration for cascading geo-hazards. This approach could provide valuable insights for disaster risk reduction and humanitarian contingency planning, where the anticipation of large-scale trends is often more important than the prediction of detailed impacts.</p> |
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| ISSN: | 1561-8633 1684-9981 |