FDPA internet of things system for forest fire detection, prediction and behaviour analysis

The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real-time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real-time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.