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<p>The rise in global temperatures is driven by greenhouse gas emissions and severe droughts, which amplify the frequency and severity of forest fires and render conventional monitoring methods expensive and unreliable. This work introduces a novel forest fire detection and prevention framework, leveraging advanced technologies and an Internet of Things (IoT) network in forest environments. Through sensors and Triangulation techniques, the technology estimates flame locations early, considering wind characteristics to minimize forest damage and enable swifter firefighting. The work uses fuzzy Logic for post-fire burnt area prediction, offering a reliable method for estimating destruction levels and enhancing disaster preparedness. An "IF… THEN" reasoning model aids effective disaster response planning. The system ensures efficiency, sustainability, and long-term monitoring through power harvesting and cutting-edge communication technologies. As forest fires expand, the study underscores the need for automated detection systems to address threats to ecosystems, worsened global warming, and ozone layer damage. The primary objective is to establish an early forest fire warning system deploying IoT modules for real-time environmental monitoring. Sensor nodes predict early-stage fires, strategically placed for optimal data evaluation. Fuzzy Logic, using FFMC, DMC, DC, and ISI values, predicts fire damage grades, contributing to a fuzzified burnt area prediction. Both methodologies show robustness, with potential improvements identified. Future developments include expanding IoT capabilities and exploring complex fuzzy logic types, aiming for an advanced, comprehensive forest fire detection and prediction system within a concise framework.</p>

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IoT and fuzzy based forest fire warning system

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Sanjoy Kumar Ghimire,
  • Rohit Kumar Kasera,
  • Shivashish Gour,
  • Tapodhir Acharjee

摘要

The rise in global temperatures is driven by greenhouse gas emissions and severe droughts, which amplify the frequency and severity of forest fires and render conventional monitoring methods expensive and unreliable. This work introduces a novel forest fire detection and prevention framework, leveraging advanced technologies and an Internet of Things (IoT) network in forest environments. Through sensors and Triangulation techniques, the technology estimates flame locations early, considering wind characteristics to minimize forest damage and enable swifter firefighting. The work uses fuzzy Logic for post-fire burnt area prediction, offering a reliable method for estimating destruction levels and enhancing disaster preparedness. An "IF… THEN" reasoning model aids effective disaster response planning. The system ensures efficiency, sustainability, and long-term monitoring through power harvesting and cutting-edge communication technologies. As forest fires expand, the study underscores the need for automated detection systems to address threats to ecosystems, worsened global warming, and ozone layer damage. The primary objective is to establish an early forest fire warning system deploying IoT modules for real-time environmental monitoring. Sensor nodes predict early-stage fires, strategically placed for optimal data evaluation. Fuzzy Logic, using FFMC, DMC, DC, and ISI values, predicts fire damage grades, contributing to a fuzzified burnt area prediction. Both methodologies show robustness, with potential improvements identified. Future developments include expanding IoT capabilities and exploring complex fuzzy logic types, aiming for an advanced, comprehensive forest fire detection and prediction system within a concise framework.