Hybrid AI System for Real-Time Forest Wildfire Prediction and Detection

Abstract

Wildfires stand as one of the most destructive natural hazards which create extensive eco- logical destruction and economic damages and endanger human safety. The capacity of traditional wildfire monitoring methods which utilize human observation and basic thermal detection systems to monitor wildfires remains restricted because of their limited spatial coverage and inability to handle multiple areas simultaneously and their lack of immediate response capabilities. The research shows a solution through its hybrid artificial intelligence framework which unites satellite fire detection systems with environmental risk prediction functions. Machine learning models assess fire susceptibility and potential spread by analyzing meteorological and environmental factors which include temperature and humidity and wind speed and topography and vegetation indices such as NDVI. The developed system uses image detection technology together with environmental risk assessment methods to enhance its abilities to track forest fires and produce early warning systems. The system architecture enables analysis through cloud ser- vices which can expand to handle both drone and remote sensing equipment operations. The assessment through experiments shows that our system delivers better prediction accuracy than traditional static prediction models. The hybrid framework shows that integrated AI systems can predict wildfires through their detection systems and they achieve intelligent environmental monitoring capabilities