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 ›› 2021, Vol. 40 ›› Issue (11): 1671-1675.

• • 上一篇    下一篇

基于加权贝叶斯网络的森林火灾风险评估

黄 琼,司 颖,王浩宇   

  1. (中国消防救援学院,北京102202)
  • 出版日期:2021-11-15 发布日期:2021-11-15
  • 作者简介:黄 琼(1985-),江西丰城人,中国消防救援学院基础部计算机教研室讲师,主要从事数据挖掘与机器学习、安全工程方面的研究工作,北京市昌平区南雁路4号,102202。

Forest fire risk assessment based on weighted Bayesian Network

HUANG Qiong, SI Ying, WANG Hao-Yu   

  1. China Fire and Rescue Institute, Beijing 102202, China
  • Online:2021-11-15 Published:2021-11-15

摘要: 基于森林火灾风险因素的不确定性及应急管理和救援力量介入对森林火灾风险管控的影响,构建森林火灾风险评估指标体系。结合领域专家的经验和知识,基于贝叶斯网络模型,绘制森林火灾风险网络图,对森林火灾发生的不确定性进行明确和处理;基于信息增益计算各指标权重,确定网络图中各指标节点的贡献值,建立加权贝叶斯网络评估模型。对我国内蒙古自治区森林火灾风险开展评估。分析结果表明,该评估模型能够有效对森林火灾的风险进行评估,具有一定的实际应用价值。

关键词: 信息增益, 贝叶斯网络, 森林火灾, 风险评估

Abstract: In view of the uncertainty among forest fire risk factors and the influence of the intervention of emergency management and rescue forces on forest fire risk control, a set of forest fire risk assessment index system is constructed. Combined with the experience and knowledge of the field experts, based on the Bayesian Network model, the forest fire risk network map is drawn to clarify and deal with the uncertainty of forest fire. The weight of each index is calculated based on the information gain, and the contribution value of each index node in the network graph is determined. The weighted Bayesian network evaluation model is established. The forest fire risk assessment in Inner Mongolia Autonomous Region of China is carried out. The experiment shows that the model can effectively realize the risk assessment of forest fire and has a certain practical value.

Key words: entropy, information gain, Bayesian Network, forest fire, risk assessment