基于机器学习的火灾事故等级分类研究

Research on fire classification based on machine learning

为了探究火灾的严重程度与消防队响应时间、救援人数、火灾地点、火灾探测器以及自喷系统等之间的关系,采用随机森林、人工神经网络、支持向量机和极限学习机4种机器学习算法对美国旧金山市的历史火灾数据进行了挖掘分析。运用模糊理论将消防队响应时间和救援人数转换为三角模糊数,提出了一种基于模糊推理的火灾因素分类方法。研究发现4种算法的准确率均超过90%,并通过交叉验证的方法证明了这些算法的可靠性。在4种算法中,随机森林算法的准确率和Kappa值均高于其他算法,但是其计算得到的第三类火灾的AUC值最小。

In order to explore the relationship between the fire severity and the response time of fire brigades,the number of rescuers,the location of fires,the operation of fire detectors and the sprinkler system,etc.,random forest,artificial neural network,support vector machine and extreme learning machine are used to classify and analyze the historical fire data of San Francisco,USA.Fuzzy theory is used to convert the response time of the fire brigade and the number of rescuers into triangular fuzzy numbers and a fire classification method based on fuzzy reasoning and data analysis is proposed.The research finds that the accuracy of the four algorithms exceeds 90%and the reliability of these algorithms is proved by cross-validation.Among the four algorithms,the accuracy and kappa value of the random forest are higher than other algorithms,but the AUC value of the third fire type in the random forest algorithm is the smallest.