On integrating complex hazard dependencies into descriptive disaster scenario generation approaches

Disaster scenarios play a crucial role in research and preparedness efforts, providing a basis to derive valuable insights into potential future disaster evolvements and impact. Scenarios are composed of events, which can be either hypothetical or derived from dedicated disaster databases that track disasters that have occurred in the past (e.g., https://www.emdat.be/). By leveraging historical data from such dedicated disaster databases, researchers have applied various statistical methods to analyze past events and their complex dependencies [1]. However, since the reality and impact of disasters are increasingly interconnected, involving multi-hazards and cascading effects, a shift towards sophisticated scenario generation methods that can capture these complex dependencies is necessary.

Building upon existing descriptive disaster scenario modeling approaches that utilize combinatorial sequence methods [2,3], we enhance the scenario generation of a disaster framework [4] with the explicit integration of complex-dependencies between hazards. We present how inter-event dependencies, event sequences that have occurred in the past as well as cascading-effects identified in the literature can be integrated into a descriptive disaster scenario generation approach. We conclude with a vision for embedding the proposed dependency-aware descriptive scenario generation approach into the bigger picture of disaster management strategies.

 

ACKNOWLEDGMENTS:
SBA Research (SBA-K1) is a COMET Centre within the COMET – Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW, and the federal state of Vienna. COMET is managed by FFG.
Moreover, this work was partly funded by the European Union under the DEP programme, Grand Agreement 101083472 and by the Federal Ministry of Labour and Economy under FFG No FO999908355.
Additionally, this work has received funding from the European Union’s Digital Europe Programme (DIGITAL) under grant agreement No 101146490.

 

REFERENCES: 
[1] Claassen, J.N. et al.: A new method to compile global multi-hazard event sets. Sci Rep 13, 13808 (2023). https://doi.org/10.1038/s41598-023-40400-5

[2] Garn, B. et al.: Combinatorial Sequences for Disaster Scenario Generation. Oper. Res. Forum 4, 50 (2023). https://doi.org/10.1007/s43069-023-00225-4

[3] Troumpoukis, A. et al.: Exploring Constraint-Based Approaches for Disaster Scenario Generation. Submitted for publication (2025)

[4] Garn, B. et al.: From Design of Experiments to Combinatorics of Disasters: A Conceptual Framework for Disaster Exercises. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds) Learning and Intelligent Optimization. LION 2022. Lecture Notes in Computer Science, vol 13621. Springer, Cham. https://doi.org/10.1007/978-3-031-24866-5_2