Zhu et al. (2026) Risk Identification and Resilience Enhancement for Rainstorm Disaster Chains: An Event Evolutionary Graph–Driven Approach

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Natural Hazards Review
• Year: 2026
• Date: 2026-08-13
• Authors: Li Zhu, Yao Lu, Yaoxing Yang, Wenya Li, Amal El Attari
• DOI: 10.1061/nhrefo.nheng-2781

Research Groups

Not specified in the provided text.

Short Summary

The study proposes an event evolutionary graph-based framework to identify critical risk nodes and propagation pathways in rainstorm disaster chains to enhance systemic resilience.

Objective

• To develop a framework for the early identification and prevention of rainstorm disaster chains by extracting causal relationships from historical data and utilizing topological metrics.

Study Configuration

• Spatial Scale: Sichuan Province (Case study).
• Temporal Scale: Not explicitly specified (based on historical disaster data).

Methodology and Data

• Models used: Event evolutionary graph-based risk network, historically calibrated topological metric.
• Data sources: Historical disaster data.

Main Results

• The event evolutionary graph-driven framework significantly outperforms conventional risk identification methods in accurately pinpointing critical risk nodes.
• The framework demonstrates superior effectiveness in enhancing systemic resilience when guiding prevention strategies.

Contributions

• Provides a novel scientific approach for identifying high-risk propagation pathways in disaster chains.
• Offers a decision-support tool for formulating targeted prevention strategies to reduce losses from rainstorm-induced disasters.

Funding

Not specified in the provided text.

Citation

@article{Zhu2026Risk,
  author = {Zhu, Li and Lu, Yao and Yang, Yaoxing and Li, Wenya and Attari, Amal El},
  title = {Risk Identification and Resilience Enhancement for Rainstorm Disaster Chains: An Event Evolutionary Graph–Driven Approach},
  journal = {Natural Hazards Review},
  year = {2026},
  doi = {10.1061/nhrefo.nheng-2781},
  url = {https://doi.org/10.1061/nhrefo.nheng-2781}
}