A Smart Prediction System for Forest Fires Using Cellular Automata and Machine Learning

Forest fires represent a widespread and devastating natural disaster affecting millions of hectares of forest land each year and posing significant risks to both human lives and property. Timely and accurate predictions of forest fire behavior are crucial for developing effective risk management strategies and improving firefighting responses. This study introduces an innovative Forest Fire Spread Behavior Prediction (FFSBP) model, which integrates two key components: the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) model. The FFSPP model leverages a combination of advanced methodologies to forecast the dynamics of fire spread, while the FFSRP model aims to predict the final outcomes of fire events.Moreover, the FFSRP model demonstrates strong predictive capabilities, particularly for smaller and medium-scale fire scenarios. These results highlight the potential of the FFSBP model as a powerful tool for improving the accuracy of forest fire predictions and supporting more effective risk management and firefighting operations.