Modelling Forest Fire Spread in the SEQ Region Using Meteorological and Environmental Datasets: A Cellular Automaton Approach

This study develops a Cellular Automaton (CA) model to predict forest fire spread in the Sunshine Coast region, utilizing diverse meteorological and environmental datasets. Key variables such as temperature, wind speed, rainfall, soil moisture, solar radiation, vegetation type, slope, elevation, and proximity to roads and streams are integrated to simulate fire dynamics with high spatial resolution. Historical fire occurrence data are used for model calibration and validation, ensuring accuracy in replicating fire spread patterns. The CA model operates through iterative cell-based transitions, governed by rules reflecting the complex interplay of environmental and meteorological factors. Results highlight the significant influence of wind, vegetation type, and topography on fire behaviour, with simulations effectively capturing spatial variability and spread dynamics. The findings underscore the model's potential as a robust, scalable tool for wildfire management, enabling data-driven planning for prescribed burns and risk mitigation. This research offers valuable insights into forest fire behaviour, contributing to sustainable ecosystem management and resilience planning in subtropical regions such as the southeast part of Queensland Australia.