主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2025, Vol. 44 ›› Issue (6): 827-833.

• • 上一篇    下一篇

基于SEM模型遴选林火驱动因子研究

于闻天, 高仲亮, 曹宇飞, 王秋华   

  1. （西南林业大学 土木工程学院，云南 昆明 650224）
  • 收稿日期:2024-07-04 修回日期:2024-10-06 出版日期:2025-06-24 发布日期:2025-06-15
  • 作者简介:于闻天，西南林业大学土木工程学院硕士研究生，主要从事林火管理方面的研究，云南省昆明市盘龙区白龙寺300号，650224。
  • 基金资助:
    国家自然科学基金资助项目（32360396）；云南省农业联合面上项目（202101BD070001-094）；“十四五”国家重点研发计划（2023YFD2202002）；国家自然科学基金资助项目（32160376）

Experimental study on the combustion characteristics and extinguishing of solvent oil spill fires

Yu Wentian, Gao Zhongliang, Cao Yufei, Wang qiuhua   

  1. (College of Civil Engineering, Southwest Forestry University, Kunming Yunan 650224, China)
  • Received:2024-07-04 Revised:2024-10-06 Online:2025-06-24 Published:2025-06-15

摘要： 森林火灾干扰森林生态系统稳定，科学评价森林火灾驱动因子是预防森林火灾的有效途径。以云南省安宁市为研究区，应用核密度分析林火空间的变化规律；建立、修正和验证结构方程（SEM）模型遴选林火驱动因子。结果表明：安宁市北部是森林火灾的高发区，有向全域扩展的趋势，火灾空间聚集程度分散，单因子无法驱动林火发生；建立修正结构方程模型来分析安宁市森林火灾风险驱动因子及权重，即夜间灯光指数（0.329）、人口密度（0.278）、GDP（0.201）、距道路距离（0.192）、最大持续风速（0.344）、平均风速（0.322）、露点（0.288）和降水量（0.106）、海拔（0.412）、坡向（0.369）和坡度（0.219），模型经过检验，预测结果准确。研究结果能够应用于森林火灾发生概率预测、森林火灾风险评估等。

关键词: 森林火灾, 空间分布, 驱动因子, 结构方程, 火灾风险

Abstract: Forest fires disrupt the stability of forest ecosystems, and scientifically evaluating the driving factors of forest fires is crucial for effective prevention. This study focuses on Anning City in Yunnan and uses kernel density analysis to investigate the spatial patterns of forest fires. We established, refined, and validated a Structural Equation Model (SEM) to identify the driving factors of these fires. Results reveal that fire incidents are concentrated in the northern part of Anning City, with a tendency to expand throughout the region. The spatial clustering of fires is dispersed, indicating that no single factor drives the occurrence of fires. The adjusted SEM identified and weighted several factors: nighttime light index (0.329), population density (0.278), GDP (0.201), distance to roads (0.192), maximum sustained wind speed (0.344), average wind speed (0.322), dew point (0.288), precipitation (0.106), elevation (0.412), aspect (0.369), and slope (0.219), the model has been validated and the predicted results are accurate. These results offer valuable data and theoretical insights for predicting fire occurrence probabilities and assessing fire risks.

Key words: forest fire, spatial distribution, driving factors, structural equation model, fire risk