Published Paper Details:

Forest fires have escalated to an environmental disaster that could not be ignored and that would be considered amongst others, a world scenario not just of the loss of flora and fauna but also of millions of people, amongst others; air quality degradation, and huge financial losses. The traditional methods of fire monitoring, such as manual patrols and lookout towers, are generally the slowest and therefore the most expensive when it comes to the impact of fires. The paper talks about the application of Artificial Intelligence (AI) in the management of forest fires focusing on three main areas: early detection, fire spread prediction, and risk prevention. AI techniques including computer vision models such as Convolutional Neural Networks (CNNs), machine learning algorithms like Random Forest and Gradient Boosting, and predictive analytics are utilised to handle the massive data from satellite imagery, drone cameras, IoT sensors, and weather parameters. The study proves that AI can be employed in real-time detection, precise fire propagation forecasting, and mapping fire-risk zones for preventive actions. Moreover, the paper presents discussions around datasets, real-life applications, challenges, and AI's future directions, thereby indicating that AI could be beneficial in terms of enhanced decision-making, better allocation of resources, and lessening both ecological and economic repercussions of forest fires.