Paper: arXiv 2511.16302

Authors: Yusheng Hu, Huaiyi Pan, Shaobo Zhong, Liying Zhang

Abstract

Wildland-Urban Interface (WUI) fires represent a compound disaster resulting from the interactions between natural ecosystems and human settlements, characterized by significantly dynamic evolving risks. However, most current risk assessment studies are based on static frameworks, which struggle to effectively capture the dynamic changes in risk over time. To address this issue, this paper proposes an innovative method that integrates a dynamic evaluation matrix, grey incidence analysis, and an optimization model for the dynamic risk assessment of WUI fires. This method incorporates time-series data by constructing a dynamic evaluation matrix, subsequently calculates the weighted standardized matrix for each evaluated area and its local volume matrices relative to the positive and negative ideal matrices. The dynamic differences between the evaluated areas and the ideal state are quantified by calculating the grey incidence degree, and an optimization model is established to solve for the superiority degree used for risk ranking. Research demonstrates that this method not only simplifies the computational process but also effectively captures the dynamic evolution patterns of fire risk across different areas, enabling refined risk classification. Compared to existing static methods, this framework overcomes their limitation in adequately representing risk dynamics, providing a more scientific basis for decision-making in the dynamic management and proactive prevention and control of WUI fires.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a mathematically structured dynamic risk assessment framework combining grey incidence analysis and optimization models. However, it lacks robust empirical validation or backtesting on real-world quantitative datasets within the provided text, leaning more theoretical than empirically demonstrated.

Research Flowchart

  flowchart TD
    A[Research Goal: Dynamic Risk Assessment of WUI Fires] --> B[Input: Time-Series Data & Real Assets]
    B --> C[Construct Dynamic Evaluation Matrix]
    C --> D[Calculate Weighted Standardized & Local Volume Matrices]
    D --> E[Grey Incidence Analysis for Dynamic Ideal State Differences]
    E --> F[Optimization Model for Superiority Degree & Risk Ranking]
    F --> G[Outcome: Dynamic Risk Classification & Proactive Management]

Cited References (32)

  1. Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment (2024) — link
  2. Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region (2024) — link
  3. The importance of geography in forecasting future fire patterns under climate change (2024) — link
  4. Integrating meteorological and geospatial data for forest fire risk assessment. (2024) — link
  5. Risk assessment of fire safety in large-scale commercial and high-rise buildings based on intuitionistic fuzzy and social graph (2024) — link
  6. Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm (2022) — link
  7. CFD-based framework for fire risk assessment of contiguous wood-frame villages in the western Hunan region (2022) — link
  8. Fire-driven vegetation type conversion in southern California. (2022) — link
and 24 more
  1. Fire risk assessment of battery transportation and storage by combining fault tree analysis and fuzzy logic (2022) — link
  2. Fire risk assessment for building operation and maintenance based on BIM technology (2021) — link
  3. A two level interval valued neutrosophic AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: An application for Istanbul (2021) — link
  4. Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework (2021) — link
  5. Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: method and case study (2021) — link
  6. Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy (2021) — link
  7. Observed Impacts of Anthropogenic Climate Change on Wildfire in California (2019) — link
  8. Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires (2019) — link
  9. Fire-regime changes in Canada over the last half century (2019) — link
  10. Global Emergence of Anthropogenic Climate Change in Fire Weather Indices (2019) — link
  11. Rapid growth of the US wildland-urban interface raises wildfire risk (2018) — link
  12. Human presence diminishes the importance of climate in driving fire activity across the United States (2017) — link
  13. Interacting effects of topography, vegetation, human activities and wildland-urban interfaces on wildfire ignition risk (2017) — link
  14. Human-started wildfires expand the fire niche across the United States (2017) — link
  15. From leaves to landscape: A multiscale approach to assess fire hazard in wildland-urban interface areas. (2016) — link
  16. Factors related to building loss due to wildfires in the conterminous United States. (2016) — link
  17. Climate-induced variations in global wildfire danger from 1979 to 2013 (2015) — link
  18. Learning to coexist with wildfire (2014) — link
  19. How risk management can prevent future wildfire disasters in the wildland-urban interface (2013) — link
  20. Prescribed burning in southern Europe: developing fire management in a dynamic landscape (2013) — link
  21. Defining the Wildland–Urban Interface (2007) — link
  22. The wildland-urban interface in the United States (2005) — link
  23. Preventing Disaster: Home Ignitability in the Wildland-Urban Interface (2000) — link
  24. The Wildland- Urban Interface Fire Problem (2010)

0 of 32 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.