AN AI AND IOT-BASED EARLY WILDFIRE RISK PREDICTION SYSTEM

Abstract

Wildfires are among the most serious environmental challenges we face today. Their damage does not end once the flames are extinguished. Forests remain scarred, animals lose their habitats, air quality worsens, and nearby communities continue to feel the effects long after the fire has vanished. Although fire monitoring technology has made progress over the years, most systems still only react after smoke or flames are visible. By that point, the fire often spreads quickly, and the chance to stop it early is mostly gone. This challenge inspired the work presented here: an early wildfire risk prediction system that uses AI and IoT to identify dangerous conditions before a fire starts. The system's concept is both simple and practical. Low- cost sensors connected to an ESP32 microcontroller continuously monitor temperature, humidity, and smoke levels. Instead of relying on fixed limits that struggle to keep pace with changing environmental conditions, the system learns what normal behavior looks like. When it detects something unusual, it treats it as an early warning sign. When these unusual patterns are identified, the system classifies the situation into three clear states: Safe, Warning, or Alert. This information is shown on a live dashboard, making it easy to understand and respond to in real-time. Test results show that this method produces fewer false alarms than traditional systems while delivering warnings earlier and more reliably. Because the system is affordable, easy to scale, and requires minimal computing power, it is especially suitable for remote forest areas, where early action can prevent significant damage.