A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning

Forest fires constitute a widespread and impactful natural disaster, annually ravaging millions of hectares of forests and posing a severe threat to human life and property. Accurate quantitative prediction of forest fire spread is essential for devising swift risk management strategies and implementing effective firefighting approaches. In response to this imperative, this study introduces a Forest Fire Spread Behavior Prediction (FFSBP) model, encompassing two integral components: the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) model. The FFSPP model involves the prediction of the direction and speed of forest fire spread, achieved through a fusion of the Cellular Automata model and the Wang Zhengfei model. On the other hand, the FFSRP model focuses on forecasting the extent of the burned area, leveraging machine learning methods. To validate the efficacy of the proposed models, a real case study is undertaken using the "3.29 Forest Fire" incident in China. The FFSPP model is validated against this case, while the FFSRP model is assessed using a real fire dataset obtained from Montesinho National Forest Park in Portugal. Results from the validation process reveal that during the natural development period of the "3.29 Forest Fire," the FFSPP model predicts a burned area of 286.81 hm(2), with an associated relative error of 28.94%. This relative error is notably smaller than those observed in the Farsite and Prometheus fire behavior simulation models. Additionally, the FFSRP model demonstrates commendable predictive performance, particularly in the context of small and medium-sized fire scenarios. These findings underscore the potential of the FFSBP model as a valuable tool in enhancing forest fire prediction accuracy and facilitating more robust risk mitigation and firefighting strategies.