Neural-Parameterized Cellular Automata for Wildfire Spread
This framework combines a three-state probabilistic Cellular Automata for wildfire spread with a Multi-Scale Convolutional Neural Network to dynamically generate spatial parameters. The model uses CNNs to extract environmental features and create variable fuel factors, allowing it to predict fire spread accurately while maintaining the physical rules of mass conservation.