Research on Fire Risk Analysis and Prevention in Wildland-Urban Interface

Authors

  • Yufei Cao
  • Zhongliang Gao
  • Wentian Yu

DOI:

https://doi.org/10.54097/v5zy0z92

Keywords:

Wildland-urban interface, Wildfire, Fire risk analysis, Fire prevention, topological structure

Abstract

In order to enhance the capability of fire risk assessment and prevention in the Wildland-Urban Interface, and to reduce the frequency and scale of fires in these border regions, ensuring the safety of residents and their properties, this paper summarizes the research conducted by domestic and international scholars. It explains the causes of wildland-urban interface fires, considering both natural and social factors. The results indicate that wildland-urban interface fires exhibit characteristics such as abundant and continuous combustible materials, complex environments, numerous ignition points, large-scale fires, rapid spread, and difficulty in firefighting. The paper clarifies the logical analysis of fire risk in the interface areas from both regional and disaster dimensions. It establishes evaluation indicators and introduces two fire assessment methods based on topological structures.  Additionally, it discusses the basis and measures for fire prevention, considering domestic and international government regulations, local standards, personnel safety evacuation, and post-fire disposal situations. The study aims to identify the shortcomings and future directions in fire risk analysis and prevention in Wildland-Urban Interface. It serves as a reference for research on fire prevention and control in these interface regions.

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References

[1] Zhao Fengjun, Ren Yumao, Shu Lifu, et al. Characteristics and prevention measures of forest fire in urban-rural fringe area [J]. Journal of Forest Fire Prevention, 2007, 25(04): 26-27+44.

[2] Qi Fangzhong. Principle and application of forest fire [M]. Beijing: China Forestry Publishing House, 2020: 24-25.

[3] Wang Qiuhua. Forest fire management [M]. Beijing: China Forestry Publishing House, 2021: 39-40.

[4] Gao Zhongliang, Chen Pengyu, Shu Lifu, et al. Analysis on disaster factors of forest and urban boundary fire [J]. Journal of Forest Fire Prevention, 2013, 31(2): 25-28.

[5] Gjedrem A M, Metallinou M M. Wildland-urban interface fires in Norwegian coastal heathlands-Identifying risk reducing measures [J]. Safety Science, 2023, 159: 1-32.

[6] Zong Xuezheng, Tian Xiaorui, Liu Chang. Forest fire risk assessment method and application at stand scale [J]. Forestry Research, 2021, 34(05): 69-78.

[7] Wang Lin, Yue Qingchun. Study on the layout of Forest Fire stations in the Forest (grassland) -urban boundary area [C]// Proceedings of the Academic Seminar on Fire Fighting and Emergency Rescue Technology in 2022. Hebei: Fire Fighting and Rescue Technical Committee of China Fire Protection Association, Rescue Command College of China People's Police University, 2022: 401-404.

[8] Jiang Chunying, Yang Xueqing, Zhang Guoli, et al. Discussion on technical system of forest fire risk assessment [J]. Forest Resources Management, 2023, 52(2): 17-26.

[9] Valero M M, Jofre L, Torres R. Multifidelity Prediction in Wildfire Spread Simulation: Modeling, Uncertainty Quantification and Sensitivity Analysis [J]. Environmental Modelling and Software, 2021, 141: 105050.

[10] Masoudvaziri N, Bardales F S, Keskin O K, et al. Streamlined wildland-urban interface fire tracing (SWUIFT): modeling wildfire spread in communities [J]. Environmental Modelling and Software, 2021, 143: 105097.

[11] Jiang Wenyu, Wang Fei, Fang Lingling, et al. Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model [J]. Environmental Modelling & Software, 2021, 135: 104895.

[12] Huang Baohua, Sun Zhijun, Zhang Hua, et al. Research on assessment methods of potential forest fire risk: A case study of Shandong Province [J]. Journal of Catastrophology, 2014, 29(04): 116-121.

[13] Hou Xiaojing, Ming Jinke, Qin Rongshui, et al. Cross-boundary fire risk analysis based on random forest model [J]. Scientia Siluricologica, 2019, 55(08): 194-200.

[14] Li Shixin, Zhang Fuquan, Lin Haifeng. Research on forest fire risk assessment based on machine learning algorithm [J]. Journal of Nanjing Forestry University (Natural Science Edition), 2023, 47(5): 49-56.

[15] Luo Dan, Wang Qingfei, Chao Bixiao, et al. Evaluation of fire risk grade at stand scale in the intersection of forest and urban areas in Guangzhou [J]. Forest Resources Management, 2023, 52(3): 56-64.

[16] Peng Wanyu, Zheng Yutao, Zhao Pan, et al. Forest fire risk level zoning based on folk fire point data: A case study of Shangli County, Jiangxi Province [J]. Science of Biohazard, 2023, 46(2): 208-215.

[17] Hou Lili, Du Wala, Yinshan, et al. Grassland fire risk assessment based on pastoral scale: A case study of Han Aobao Gacha, East Wuqi [J]. Acta Ecologica Sinica, 2022, 42(3): 1059-1070.

[18] Song Yinghua, Zhang Xiaoying, Lv Wei. Construction and risk analysis of fire disaster chain network in urban and forest boundary [J]. China Work Safety Science and Technology, 2020, 16(5): 122-128.

[19] Chuvieco E, Aguado I, Jurdao S, et al. Integrating geospatial information into fire risk assessment [J]. International Journal of Wildland Fire, 2013, 23: 606-619.

[20] Ronchi E, Gwynne S M V, Rein G, et al. An open multi-physics framework for modelling wildland-urban interface fire evacuations [J]. Safety science, 2019, 118: 868-880.

[21] Wahlqvist J, Ronchi E, Gwynne S M V, et al. The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform [J]. Safety science, 2021, 136: 105145.

[22] Ronchi E, Wahlqvist J, Ardinge A, et al. The verification of wildland–urban interface fire evacuation models [J]. Natural hazards, 2023, 117: 1493-1519.

[23] Yue Jinzhu, Feng Zhongke, Jiang Wei. Research on forest fire disaster risk management based on fuzzy integral assessment model [J]. Journal of Beijing Forestry University, 2005, 27(S2): 177-181.

[24] Luo Xiangyu, Li Zongmin. Forest fire risk assessment based on hesitation fuzzy language [J]. Forest Resources Management, 2020, 49(1): 183-190.

[25] Tian Guanghui, Chen Huilin, Xu Xiangchun. Research on forest fire risk grade prediction based on fuzzy comprehensive discrimination [J]. Journal of Catastrophology, 2013, 28(03): 117-122.

[26] Lu Hao, Zhang Hongzhou, Zhong Han, et al. Bayesian network parameter learning method integrating expert comprehensive knowledge [J]. Science Technology and Engineering, 2021, 21(21): 8944-8950.

[27] Dlamini W M. Application of Bayesian networks for fire risk mapping using GIS and remote sensing data [J]. GeoJournal, 2011, 76: 283-296.

[28] Bai Haifeng, Liu Xiaodong, Niu Shukui, et al. Research on forest fire prediction model construction based on Bayesian model average method: A case study of Dali, Yunnan Province [J]. Journal of Beijing Forestry University, 2021, 43(5): 44-52.

[29] Huang Qiong, Si Ying, Wang Haoyu. Forest fire risk assessment based on weighted Bayesian network [J]. Fire Science and Technology, 2021, 40(11): 1671-1676.

[30] Xiao Yundan, Ji Ping. Prediction of forest fire occurrence based on Bayes-zero expansion negative binomial model [J]. Journal of Central South University of Forestry and Technology, 2021, 41(5): 49-56.

[31] Zheng Wenxia, Guo Xin-Bin, Guo Lin-Fei, et al. Summary of wildland-urban boundary fire management in the United States and its implications for China [J]. Chinese Journal of Ecology, 2020, 39(1): 300-307.

[32] Soto M E C, Martínez J R M, Bonilla S, et al. Calculating minimum safety distance against wildfires at the wildland-urban interface in Chile and Spain [J]. Heliyon, 2022, 8(11):11238.

[33] Hebei Market Supervision and Administration Bureau. About the forest fire safety guidelines - town/boundaries in hebei province local standard open to advice notice [EB/OL]. (2021.12.12) [2023.09.20]. http://scjg.hebei.gov.cn/info/77603.

[34] Zhou Jing, Si Liqing, Wang Chenghu, et al. Forest fire prevention and population evacuation measures in boundary areas [J]. Protected Nature Areas, 2022, 2(4): 109-115.

[35] Folk L H, Kuligowski E D, Gwynne S M V, et al. A provisional conceptual model of human behavior in response to wildland-urban interface fires [J]. Fire technology, 2019, 55: 1619-1647.

[36] McCaffrey S, Toman E, Stidham M, et al. Social science research related to wildfire management: an overview of recent findings and future research needs [J]. International Journal of Wildland Fire, 2012, 22(1): 15-24.

[37] He Zheng, Chen Huihua, Yan Hongyan, et al. Scenario-based comprehensive assessment for community resilience adapted to fire following an earthquake, implementing the analytic network process and preference ranking organization method for enriched evaluation II techniques [J]. Buildings, 2021, 11(11): 523.

[38] Lopes R F R, Rodrigues J P C, Camargo A L, et al. Resilience of Industrial Buildings to Wildland-Urban Interface Fires [C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2022, 1101(2): 022034.

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Published

06-02-2025

How to Cite

Cao, Y., Gao, Z., & Yu, W. (2025). Research on Fire Risk Analysis and Prevention in Wildland-Urban Interface. Highlights in Science, Engineering and Technology, 127, 115-122. https://doi.org/10.54097/v5zy0z92