# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 5 Conclusions  
This paper proposes a dynamic risk-modeling method that combines SPN and Bayesian theory and which is used to simulate and analyze natural fire accidents in cities. Section 1 mainly introduces the principle of the method. In Section 2, the stochastic Petri nets are automatically generated through natural language texts to effectively simulate the accident process and identify the key nodes. In Section 3, Bayesian theory is applied to dynamically update the SPN model and accurately estimate the occurrence probabilities of events and the impacts of key nodes.  
The research results show that the model has important application significance in formulating preventive measures and emergency strategies. It not only improves the efficiency of fire prevention and control, but also helps to reduce the potential losses caused by fires. Effective emergency strategies are crucial for mitigating the impacts of natural fires and enhancing urban resilience. These strategies encompass a series of proactive and reactive measures, including early-warning systems, resource allocation, evacuation planning, and post-disaster recovery. In the context of smart cities, the integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) [23], and real-time data analytics has revolutionized emergency response by enabling faster decision making and more efficient resource deployment.  
Establishing robust fire detection and monitoring systems is essential for identifying potential fire hazards at their nascent stages. These systems utilize sensors, satellite imagery, and predictive models to provide timely alerts, enabling authorities to take preventive actions before fires escalate. The efficient allocation of emergency resources, such as fire-fighting equipment, medical supplies, and personnel, is of great significance for minimizing response times. Coordination among various stakeholders, including government agencies, emergency services, and community organizations, ensures a unified and effective response. Well-defined evacuation routes and shelter locations are vital for ensuring the safety of affected populations. Smart city technologies, such as real-time traffic management and mobile applications, can facilitate orderly evacuations and reduce the risk of casualties. Recovery efforts focus on restoring infrastructure, providing humanitarian aid, and addressing long-term ecological and economic impacts. Building resilience involves learning from past incidents, strengthening urban planning, and promoting community awareness and preparedness.  
By integrating these strategies into the dynamic risk-modeling framework, this study aims to enhance the adaptive capacity of smart cities to respond to natural fire incidents and, ultimately, reduce their social and economic impacts.  
Conceptualization, M.H. and F.N.; methodology, M.H. and F.N.; validation, M.H., F.N. and W.L.; formal analysis, M.H. and J.L.; data collection, N.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by the National Natural Science Foundation of China under grant 12371508, the Collaborative Education Project of the Ministry of Education under grant 241103760141654, and the Shanghai Students' Platform for innovation and entrepreneurship training program under grant XJ2924166, XJ2024151.  
Not applicable.  
Data Availability Statement: Data available on request due to privacy.  
The authors declare no conflicts of interest.