Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-267-2025
https://doi.org/10.5194/nhess-25-267-2025
Research article
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20 Jan 2025
Research article | Highlight paper |  | 20 Jan 2025

Impacts from cascading multi-hazards using hypergraphs: a case study from the 2015 Gorkha earthquake in Nepal

Alexandre Dunant, Tom R. Robinson, Alexander L. Densmore, Nick J. Rosser, Ragindra Man Rajbhandari, Mark Kincey, Sihan Li, Prem Raj Awasthi, Max Van Wyk de Vries, Ramesh Guragain, Erin Harvey, and Simon Dadson

Model code and software

CompulsoryCoffee/Multi-Hazard-Risk-Analysis-Using-Hypergraphs: v2024 (Version V2024) CompulsoryCoffee https://doi.org/10.5281/zenodo.14650782

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Executive editor
This study introduces a new approach to multi-hazard risk assessment, leveraging hypergraph theory to model 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. Conceptualising and visualising risks within a hypergraph framework overcomes these limitations, enabling efficient simulation of multi-hazard interactions and their impacts on infrastructure. The authors apply their model to the 2015 Mw 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. Their 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 anticipation of large-scale trends is often more important than prediction of detailed impacts.
Short summary
Natural hazards like earthquakes often trigger other disasters, such as landslides, creating complex chains of impacts. We developed a risk model using a mathematical approach called hypergraphs to efficiently measure the impact of interconnected hazards. We showed that it can predict broad patterns of damage to buildings and roads from the 2015 Nepal earthquake. The model's efficiency allows it to generate multiple disaster scenarios, even at a national scale, to support preparedness plans.
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