A Digital Twin Approach to Forest Fire Re-Ignition: Mechanisms, Prediction, and Suppression Visualization

Statistics indicate that over 90% of large forest fires experience re-ignition after initial extinction. However, research on the mechanisms triggering forest fire rekindling remains largely empirical, lacking an intuitive 3D mathematical model to elucidate the process. To fill this gap, this study proposes a digital twin-based forest fire re-ignition trigger model to investigate the transition from smoldering to flaming combustion. Leveraging digital twin technology, a virtual forest environment was constructed to assess the influence of ambient wind conditions and terrain slope on the smoldering-to-flaming (StF) transition based on historical rekindling data. Subsequently, logistic regression was employed in a reverse iterative process to update the model parameters, thereby establishing a matching mechanism between the model predictions and the observed rekindling states. This approach enables the adaptive adjustment of the weights assigned to key variables (e.g., wind speed and slope) and facilitates the prediction of forest fire rekindling probability within the virtual environment. Additionally, digital twin simulations are employed to assess the 3D firefighting effectiveness of unmanned aerial vehicles (UAVs) deploying hydrogel and solidified foam extinguishing agents. This visualization of the firefighting process provides valuable insights, aiding in the development of more effective strategies for preventing and controlling fire re-ignition.