Listening to the Forest: AI-Driven Meteo-Acoustic Early WarningSystem for Wildfire Detection

Wildfires have devastating environmental, economic, and human impacts. For example, the recent wildfire in Los Angeles is estimated to have
caused over $250 billion in damages and claimed dozens of lives. The devastating 2019–2020 Australian bushfires reportedly led to the loss of
over one billion mammals, birds, and reptiles, marking one of the most severe wildlife disasters ever recorded.

Every extreme wildfire begins as a small one. The critical early stage of a wildfire offers a window of opportunity for firefighters to contain it before
it grows out of control. Early detection is crucial in mitigating damage, yet existing methods, such as satellite monitoring and thermal imaging, face limitations – including delayed detection, coverage gaps, and inefficiency in dense vegetation or nighttime conditions.

This proposal presents a Meteo-Acoustic Early Warning System for Wildfire Detection. The system is inspired by the natural world, where sound is
often the earliest sign of danger. Fire has unique sounds, such as the crackle of embers or the snap of dry wood, which can be detected by AIenhanced
acoustic sensors. The system represents a new class of meteorological sensors that couple atmospheric conditions with AI-driven acoustic sensing to detect ignition events and dynamically assess fire risk.

By deploying a network of low-cost acoustic sensors in wildfire-prone areas, this system can significantly enhance early detection capabilities. Machine learning models trained on fire-specific audio patterns will distinguish wildfire sounds from background noise, improving response times and reducing false alarms. Additionally, it can be operational in all conditions, including nighttime or dense forests, where vision-based systems often fail

The system is enhanced by integrating real-time meteorological data, including fire weather indices, to increase prediction accuracy. Furthermore, incorporating wind speed and direction allows for more accurate triangulation of sound sources, as these factors significantly influence how acoustic signals travel through the environment.

By combining real-time sound analysis with meteorological data, this system offers faster, more reliable detection in low-visibility conditions and at lower cost than traditional methods. Its integration of atmospheric context, acoustic triangulation, and AI-enhanced pattern recognition can significantly improve emergency response, reduce ecological damage, and save lives.