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 (9): 1292-1297.

• • 上一篇    下一篇

基于GA-BP神经网络的兴安落叶松枯落物引燃概率研究

贾慧敏1, 辛颖1, 池泓颖1, 张德新2   

  1. （1. 东北林业大学 机电工程学院，黑龙江 哈尔滨 150040；2. 长白山森工集团，吉林 延吉 130114）
  • 出版日期:2023-09-15 发布日期:2023-09-15
  • 作者简介:贾慧敏（1999- ），女，内蒙古乌兰察布人，东北林业大学机电工程学院硕士研究生，主要从事林业工程方面的研究，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 ignition probability of Larix gmelinii litter based on GA-BP neural network

Jia Huimin1, Xin Ying1, Chi Hongying1, Zhang Dexin2   

  1. (1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Heilongjiang Harbin 150040, China;2. Changbaishan Forest Industry Group, Jilin Yanji 130114, China)
  • Online:2023-09-15 Published:2023-09-15

摘要： 基于室内试验数据，研究风速、可燃物含水率和飞火源尺寸对引燃概率的影响，探究GA-BP神经网络模型预测引燃概率的适用性，为森林飞火行为研究提供理论基础。以兴安落叶松地表可燃物为燃烧材料，以兴安落叶松树枝为飞火源，调节风速、可燃物含水率和飞火源尺寸进行燃烧试验。通过SPSS分析多种因素对引燃概率的影响程度，采用遗传算法优化BP神经网络，建立GA-BP神经网络模型预测引燃概率，模型以风速、可燃物含水率和飞火源尺寸为输入变量，以引燃概率为输出变量，选择最优隐含层神经元个数进行模型训练和预测，与BP神经网络模型比较预测能力。结果表明，引燃概率与风速呈正相关趋势，与可燃物含水率呈负相关趋势，大直径飞火源的引燃概率显著；单因素引燃能力由强到弱为：风速>飞火源尺寸>可燃物含水率；多因素交互作用引燃能力由强到弱为：风速与飞火源尺寸>可燃物含水率与飞火源尺寸>风速与可燃物含水率>风速、可燃物含水率与飞火源尺寸；GA-BP神经网络模型预测能力好，有利于森林火灾飞火预测。

关键词: 兴安落叶松, 枯落物, 风速, 含水率, 引燃概率, GA-BP神经网络

Abstract: Based on indoor experimental data, study the effects of wind speed, moisture content of fuel and size of flying fire source on the ignition probability, and explore the applicability of GA-BP neural network model to predict the ignition probability, to provide a theoretical basis for the study of forest flying fire behavior. Using the surface fuel of Larix gmelinii as the combustion material and the branches of Larix gmelinii as the flying fire source, the combustion experiment was conducted by adjusting the wind speed, moisture content of fuel and the size of the flying fire source. SPSS is used to analyze the influence of various factors on the ignition probability. Genetic algorithm is used to optimize the BP neural network, and GA-BP neural network model is established to predict the ignition probability. The model takes wind speed, moisture content of fuel and the size of the ignition source as input variables, and the ignition probability as output variables. The optimized number of neurons in the hidden layer is selected to train and predict the model, and the prediction ability is compared with the BP neural network model. Results showed that, the ignition probability has a positive correlation with the wind speed and a negative correlation with the moisture content of the fuel. The ignition probability of the large-diameter flying ignition source is significant; The single factor ignition ability from strong to weak is as follows: wind speed>size of fly ignition source>moisture content of fuel; The strong or weak ignition ability of multi-factor interaction is: wind speed and fly ignition source size>moisture content of combustible and flying fire source size>wind speed and moisture content of combustible>wind speed, moisture content of combustible and size of flying fire source; the prediction ability of the model is good, which is conducive to the prediction of forest fires.

Key words:  Larix gmelinii, litters, wind velocity, water content, ignition probability, GA-BP neural network