Building fire risk assessment based on machine learning
Authors: Aiming Xu, Beibei Sun
Abstract: In recent years, with the rapid development of economy, the continuous expansion of trading areas and buildings has caused more serious fire risks. In order to reduce the incidence of fire accidents and effectively improve building fire safety management, it is necessary to explore the application of the machine learning (ML) algorithms in fire risk assessment. This study aims to propose a ML framework for building quantitative fire risk assessment and use four regression algorithms with the data set which is collected by the Fire Safety Management System of Social Units in Jiangsu Province to predict fire risk score of each company and the Mean Square Error (MSE) is used to evaluate the models. The final result shows DNN has the best performance in the experiment, which is of great significance to promote the intelligence and accuracy of fire prevention and control in smart city construction.
Keywords: Building Fire Risk, Risk Assessment, Machine Learning, Regression Algorithm, Deep Learning
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