Early Wildfire Detection using Sensor Networks and RNN-Based Models

Wildfires pose a growing threat to ecosystems, human life, infrastructure, and the global climate, exacerbated by rising greenhouse gas emissions. Early detection is critical for timely response and mitigation, yet traditional surveillance methods remain limited in sensitivity, scalability, or reliability. In this paper, we present a low-power, solar-enabled wireless sensor network designed to monitor environmental parameters associated with wildfire ignition and spread. Sensor data is analyzed using Recurrent Neural Networks (RNNs), which are well-suited for capturing temporal patterns in sequential data streams. Our system enables early detection of fire events by recognizing subtle precursors in sensor readings, allowing for rapid alerts and emergency intervention. We demonstrate a proof-of-concept implementation and discuss the system's effectiveness, limitations, and opportunities for future large-scale deployment. The integration of custom hardware and time-series AI models offers a promising framework for real-time, autonomous wildfire surveillance