Identifying Woodland Wildfires By applying a Method Combining Cell-Based Models and Machine Learning

As an important natural hazard, forest fires ruin enormous tracts of forest every year, putting ecosystems, people, and property at risk. For optimal firefighting and risk mitigation, timely and precise fire spread prediction is essential. The two components of the Forest Fire Spread Habit Prediction (FFSBP) system—the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) system—are shown in this paper. To predict the direction and rate of fire spread, the FFSPP model blends the Wang Zhengfei model with Cellular Automata. To anticipate the extent of the burned area, the FFSRP model use collective machine learning techniques. Verification of the “3.29 Forest Fire” instance in China and data from Portugal’s Montesinho National Forest Park show better accuracy Validation with data from Portugal’s Montesinho National Forest Park and the “3.29 Forest Fire” instance in China reveals more precise results over current models, notably for small to medium fires. It is plenty of potential to better forest fire management procedures with the provided norm.