Abstract: 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 reli ability 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. 

Key words: data mining, machine learning, fire accident data, fuzzy theory