Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of w...Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.展开更多
The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing...The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces.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).
文摘Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.
基金This work is funded by the Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N)the PolyU Emerging Frontier Area(EFA)Scheme of RISUD(P0013879).
文摘The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces.
