A Hybrid Modeling Approach for Forest Fire Prediction Based on Cellular Automata and Machine Learning Algorithms

Abstract

Forest fires pose a serious threat to natural ecosystems, wildlife, human life, and property, making early prediction and effective management essential. Traditional fire prediction methods often rely on historical data or simple statistical models, which may not accurately capture the complex and dynamic nature of fire occurrence and spread. This project presents a hybrid modeling approach for forest fire prediction that integrates Cellular Automata and machine learning algorithms. Cellular Automata are used to simulate the spatial and temporal spread of fire based on local interactions and environmental conditions, while machine learning models analyze historical and real-time data to predict fire risk and ignition probability. By combining simulation-based modeling with data-driven learning, the proposed system improves prediction accuracy and provides a more realistic representation of fire behavior. The hybrid approach supports early warning, risk assessment, and decision-making for forest fire prevention and management, offering a scalable and intelligent solution for reducing the impact of forest fires.