The Modeling of Fire Scenario Deduction in Commercial Complexes by Bayesian Network

Jianyu Zhao, Linghan Meng

2022

Abstract

The fire evolution process in commercial complex buildings is complicated, and it is often difficult for firefighters to take targeted emergency measures when a disaster occurs. In order to solve this problem effectively, this paper combines Bayesian network with scenario deduction to deduce the evolution path of fire accidents in commercial complex buildings. On the basis of specifying the scenario deduction elements, the Bayesian network joint probability distribution is used to calculate the scenario state probability, so as to obtain the current state of the accident and the possible future evolution trend. The example results show that the model can directly show the fire evolution process of commercial complex, and provide a reliable basis for fire emergency decision-makers to take timely and effective emergency plans.

Paper Citation

in Harvard Style

Zhao J. and Meng L. (2022). The Modeling of Fire Scenario Deduction in Commercial Complexes by Bayesian Network. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 796-801. DOI: 10.5220/0011767900003607

in Bibtex Style

@conference{icpdi22,
author={Jianyu Zhao and Linghan Meng},
title={The Modeling of Fire Scenario Deduction in Commercial Complexes by Bayesian Network},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={796-801},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011767900003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - The Modeling of Fire Scenario Deduction in Commercial Complexes by Bayesian Network
SN - 978-989-758-620-0
AU - Zhao J.
AU - Meng L.
PY - 2022
SP - 796
EP - 801
DO - 10.5220/0011767900003607
PB - SciTePress