Machine Learning and Cellular Automata Based Approach for Predicting Forest Fires

Abstract

Forest fires pose a serious threat to ecosystems, wildlife, and human settlements, leading to
significant environmental and economic losses each year. Accurate prediction of forest fires is
essential for effective prevention and mitigation strategies. This study presents a machine
learning and cellular automata based approach for predicting forest fires by analyzing
environmental and spatial factors that influence fire occurrence and spread. Machine learning
algorithms are employed to analyze historical fire data and meteorological parameters such as
temperature, humidity, wind speed, and vegetation conditions to predict the probability of fire
occurrence. Cellular Automata are utilized to simulate the spatial propagation of fire by
modeling the interactions between neighboring cells in a forest landscape. The integration of
machine learning with cellular automata enables both accurate prediction and dynamic
simulation of fire spread patterns. This hybrid approach improves the understanding of fire
behavior and supports early warning systems for forest management authorities. The
proposed model enhances prediction accuracy and provides valuable insights for disaster
management and environmental protection. Experimental analysis demonstrates that the
combined methodology performs more effectively than traditional prediction methods,
making it a reliable tool for forest fire risk assessment and monitoring