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The need to create cutting-edge technologies for prompt and precise forest fire identification has grown as concerns about environmental sustainability and catastrophe preparedness spread around the globe. In order to improve forest fire detection and mitigation tactics, this study suggests a novel method that utilizes the Internet of Things (IoT) and Artificial Intelligence (AI) capabilities in concert. Real-time data collecting from forested regions is made possible with the help remote sensors. The sensors communicate with each other in a mesh topology within the LoRaWAN. The machine learning enables very accurate analysis of the data generated and then the data's are pushed to the cloud, in order to identify future fire occurrences. The system works by continuously observing environmental factors, learning to differentiate between typical conditions and aberrations brought on by fire. Predictive modeling is also included in the suggested framework; allowing early fire risk assessment based on past data patterns. This study emphasizes the importance of fusing these technologies to transform the way forest fire management is done, by providing proactive ways to lessen the disastrous effects of such disasters on ecosystems and people's livelihoods. The proposed method not only illustrates a feasible route for identifying forest fires but also creates a framework for utilizing IoT and AI in tackling challenging environmental issues. Cost effective, accuracy and quick fire detection are the main highlights of the proposed methodology.
Mohanty et al. (2024) studied this question.