AI-Driven Forest Fire Prediction and Monitoring System: Enhancing Early Detection and Response

Forest fires are a major threat to ecosystems, wildlife, and human communities that require a sophisticated approach for early detection and real-time monitoring. This paper introduces an AI-based forest fire forecast and monitoring system that utilizes machine learning, satellite imaging, and IoT-based sensors to improve wildfire prevention and response. The system employs deep learning models to process meteorological data, vegetation indices, and past fire trends to make precise predictions of fires breaking out. Moreover, real-time observation is realized through thermal imaging using drones and processing through the cloud, allowing timely decision-making. The envisaged framework is tested using data from several forest areas, proving to be highly accurate in detecting fires and predictive modeling. The research underscores the efficacy of AI in addressing wildfire threats and offers insights for future improvements to enable large-scale deployment.