# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 1 Introduction  
Urban safety, fundamental to a city's normal operation, faces growing risks of disaster accidents with the development of the smart city concept. Natural fires, which are prevalent in urban areas, especially arid or vegetated regions, pose significant threats to the environment, social economy, and ecology, causing catastrophic consequences including loss of life, property, and long-term ecological damage [1]. Their evolution involves complex interactions among environmental factors, human activities, and urban infrastructure, making them an ideal subject for dynamic risk modeling to gain insights into event sequences and probabilistic outcomes crucial for risk assessment and management. Fire accidents also have the advantage of more comprehensive records compared to other disasters, with a large amount of historical data on causes, development patterns, and response strategies,all of which are vital for developing and validating dynamic risk models based on stochastic Petri nets to provide accurate risk predictions and effective prevention and control measures. As an integral part of smart city safety strategies, urban fire prevention and control can be directly supported by fire accident research in formulating practical risk management policies and emergency response plans. Despite the significance of other disasters like floods and earthquakes, the unique dynamic evolution characteristics and far-reaching social impacts of fire accidents make them the prime focus of this research, enabling the development of targeted solutions to mitigate urban natural fire risks and enhance smart city safety and resilience. The prevention and control of urban fires are integral components of smart city safety strategies. Research on fire accidents can directly support the formulation of practical risk management policies and emergency response plans. Although other disasters like floods and earthquakes are equally significant, the unique dynamic evolution characteristics and far-reaching social impacts of fire accidents make them a prime focus of this research. This focus enables us to develop targeted solutions to mitigate the risks associated with urban natural fires and enhance the overall safety and resilience of smart cities. There is a need to promptly comprehend the influence of natural fires on urban forests and their socio-ecological dynamics across time and space. Nevertheless, local and regional decision-makers frequently lack information regarding the dynamic alterations in urban forest cover, structure, and composition induced by fires and how such changes might affect the ecosystem. The safety, reliability, and risk analysis of the ecosystem are crucial tasks executed throughout the system's life cycle to guarantee the reliability of the safety system. Currently, the primary research on accident risk detection centers around the detection of diverse risk patterns and causes, and the dynamic evolution process of risk remains a challenging issue in risk prevention. Simultaneously, in the domain of emergency rescue, owing to the incompleteness of information at the accident site, the complexity of the rescue environment, and the uncertainty of the accident evolution, rescue work is encountering unprecedented difficulties. An in-depth exploration into the characteristics of rescue time holds great significance for enhancing rescue efficiency and minimizing losses [2]. Currently, numerous scholars have engaged in research on urban risk assessment [3; 4]. have harnessed information technology by means of collecting, analyzing, sharing, and formally modeling real-time data, thereby providing decision-makers with more comprehensive, accurate, and timely informational support. References [5; 6] introduced the Color Petri Net (CPN) [7], which surmounts the stringent time and combination constraints encountered in complex event sequence modeling and is extensively employed in the domains of risk analysis and accident modeling.