Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physic...Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.展开更多
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N)Furthermore,this research was undertaken with the assistance of computational resources from the National Computational Infrastructure(NCI Australia),an NCRISenabled capability supported by the Australian Government.
文摘Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.
