Scholarlink

<p>Wildfires pose an increasing threat to both the environment and human communities, necessitating efficient systems for early detection and rapid response. The rapid propagation of fires and the need for dynamic resource allocation make fire monitoring a complex and critical challenge. This study presents the modeling and performance evaluation of a fire monitoring system using Stochastic Petri Nets (SPN). The proposed model accurately represents the operational dynamics of a real system, enabling the analysis of key performance metrics, including mean response time (MRT), throughput (TP), drop probability (DP), and resource utilization. Additionally, a sensitivity analysis based on Design of Experiments (DoE) is conducted to identify the most influential factors affecting system performance. Statistical validation showed agreement between the SPN model and the real system (MRT = 1.2013 vs. 1.2016; p-value = 0.9528). In the performance analysis, CFC = 4 kept MRT below 1.40, achieved throughput of about 2.10 pkgs/s, and reduced drop probability, which reached up to 80% with CFC = 1 under high load. These results underscore the applicability of SPN modeling as a robust methodology for optimizing fire monitoring and response strategies, ultimately contributing to the mitigation of wildfire impacts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing wildfire response: an SPN-based approach to performance and sensitivity analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

查看全文
  • Arthur Sabino,
  • Luiz Nelson Lima,
  • Vandirleya Barbosa,
  • Leonardo Freitas,
  • Priscila Solis Barreto,
  • Marcos F. Caetano,
  • Francisco Airton Silva

摘要

Wildfires pose an increasing threat to both the environment and human communities, necessitating efficient systems for early detection and rapid response. The rapid propagation of fires and the need for dynamic resource allocation make fire monitoring a complex and critical challenge. This study presents the modeling and performance evaluation of a fire monitoring system using Stochastic Petri Nets (SPN). The proposed model accurately represents the operational dynamics of a real system, enabling the analysis of key performance metrics, including mean response time (MRT), throughput (TP), drop probability (DP), and resource utilization. Additionally, a sensitivity analysis based on Design of Experiments (DoE) is conducted to identify the most influential factors affecting system performance. Statistical validation showed agreement between the SPN model and the real system (MRT = 1.2013 vs. 1.2016; p-value = 0.9528). In the performance analysis, CFC = 4 kept MRT below 1.40, achieved throughput of about 2.10 pkgs/s, and reduced drop probability, which reached up to 80% with CFC = 1 under high load. These results underscore the applicability of SPN modeling as a robust methodology for optimizing fire monitoring and response strategies, ultimately contributing to the mitigation of wildfire impacts.