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 ›› 2023, Vol. 42 ›› Issue (6): 823-828.

• • 上一篇    下一篇

基于Logistic回归的樟子松林飞火引燃概率模型研究

辛颖1, 刘家豪1, 侯文晟2, 谷晨1   

  1. （1. 东北林业大学 工程技术学院，哈尔滨 150040；2. 江苏卫华海洋重工有限公司，江苏 南通 226200）
  • 出版日期:2023-06-15 发布日期:2023-06-15
  • 作者简介:作者简介：辛 颖（1977- ），女，吉林德惠人，东北林业大学工程技术学院副教授，主要从事森林工程研究，黑龙江省哈尔滨市香坊区和兴路26号，150040。
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目（2572015CB05）；黑龙江省自然科学基金联合引导项目（LH2020C047）

Study on the probability model of flying fire ignition of Pinus sylvestris var. mongholica Litv forest based on Logistic regression

Xin Ying1, Liu Jiahao1, Hou Wensheng2, Gu Chen1   

  1. (1. College of Engineering and Technology, Northeast Forestry University, Heilongjiang Harbin 150040, China;2. Jiangsu Weihua Ocean Heavy Industry Co., Ltd., Jiangsu Nantong 226200, China)
  • Online:2023-06-15 Published:2023-06-15

摘要： 飞火是森林火灾蔓延的重要途径，为探究飞火引燃的形成机理，以樟子松松针为材料制作燃料床，樟子松树枝、树皮和松果为飞火火源，改变风速、飞火火源尺寸和燃料床压缩比，进行飞火引燃试验。通过SPSS选取最优建模数据，基于二元Logistic回归分析，建立引燃概率模型。3种飞火火源各进行1 920次试验，共5 760次试验，树枝、树皮和松果分别引燃336次、304次和376次。各交互作用的引燃次数曲线均呈上升趋势，且风速在其他3种试验条件的交互作用中，均起到显著影响。树枝的建模样本预测准确率为75.6%，总预测准确率为77.3%；树皮的建模样本引燃预测准确率为77.8%，总预测准确率为83.6%；松果的建模样本引燃预测准确率为81.2%，总预测准确率为85.4%。3种飞火火源引燃能力由强到弱分别为：松果>树枝>树皮，3种飞火火源的引燃概率模型的总预测率在77.3%~85.4%之间，均具有较高的准确率，可为森林防火工作提供理论参考。

关键词: 樟子松, 飞火, 概率模型, Logistic

Abstract: Flying fires are an important way of spreading forest fires. In order to investigate the formation mechanism of flying fire ignition, this paper uses camphor pine pine needles as the material to make a fuel bed and camphor pine twigs, bark and pine cones as the flying fire source, and changes the wind speed, the size of the flying fire source and the compression ratio of the fuel bed to conduct flying fire ignition experiments. The optimal modelling data was selected by SPSS and the ignition probability model was developed based on binary logistic regression analysis. A total of 5 760 experiments were conducted for 1 920 experiments for each of the three fly fire sources, with 336, 304 and 376 ignitions for twigs, bark and pine cones respectively. The ignition number curve for each interaction showed an increasing trend and wind speed played a significant influence in the interaction of the other three experimental conditions. The prediction accuracy for the modeled sample of twigs was 75.6% and the total prediction accuracy was 77.3%; the prediction accuracy for the modeled sample of bark ignition was 77.8% and the total prediction accuracy was 83.6%; and the prediction accuracy for the modeled sample of pine cones ignition was 81.2% and the total prediction accuracy was 85.4%. The total prediction rates of the three fire ignition sources were 77.3% to 85.4%, all with high accuracy, which can provide theoretical reference for forest fire prevention.

Key words: Pinus sylvestris var. mongholica Litv, flying fire, probability model, Logistic