Revolutionizing Fire Detection: Harnessing the Power of Machine Learning Models

The increasing frequency of wildfires and industrial accidents has highlighted the urgent need for reliable, real-time fire detection systems. Traditional fire detection technologies, primarily relying on smoke and heat sensors, often fail in dynamic and complex environments, making them less effective. In contrast, Artificial Neural Networks (ANNs) and Recurrent Neural Networks (RNNs) offer significant advantages for fire and smoke detection, as they can process large amounts of data and adapt to varying conditions, improving detection accuracy and robustness. This study provides a comparative evaluation of ANN and RNN models for fire detection, focusing on their ability to handle challenging scenarios such as occlusion and complex environmental conditions, where false alarms must be minimized. We explore the integration of these models with visual, thermal, and text data to enhance the detection process.