2020 | OriginalPaper | Buchkapitel
Application of Bayesian Belief Networks for Smart City Fire Risk Assessment Using History Statistics and Sensor Data
Autoren: Jinlu Sun, Hongqiang Fang, Jiansheng Wu, Ting Sun, Xingchuan Liu
Verlag: Springer Singapore
Erschienen in:
Data Science
Abstract
Fires become one of the common challenges faced by smart cities. As one of the most efficient ways in the safety science field, risk assessment could determine the risk in a quantitative or qualitative way and recognize the threat. And Bayesian Belief Networks (BBNs) has gained a reputation for being powerful techniques for modeling complex systems where the variables are highly interlinked and have been widely used for quantitative risk assessment in different fields in recent years. This work is aimed at further exploring the application of Bayesian Belief Networks for smart city fire risk assessment using history statistics and sensor data. The dynamic urban fire risk assessment method, Bayesian Belief Networks (BBNs), is described. Besides, fire risk associated factors are identified, thus a BBN model is constructed. Then a case study is presented to expound the calculation model. Both the results and discussion are given.
