Fusing Innovation and Nature: Empowering Forest Fire Detection and Prediction Through IoT Enabled Edge Computing and Deep Learning

In the intricate interplay of nature and technology, an imperative arises to synergize innovation with ecological stewardship. This research seeks to harmonize technology and environmental conservation, focusing on improving forest fire detection and prediction by answering three major research questions. The research combines Deep Learning, Edge Computing, and the Internet of Things (IoT) to enhance its applicability in various landscapes. The approach blends Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process data from strategically placed sensors, including satellite images, UAVs, drones, weather data, terrain information, historical fire records, and real-time sensor readings. Unlike traditional methods, this study emphasizes edge computing for rapid on-site data analysis to minimize latency. It calls for proactive strategies, empowered by technology, to mitigate devastating fires and protect the environment. The outcomes showcase the potential of this integration, offering a promising solution to combat forest fires and maintain the delicate balance of nature and its ecosystems.