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EnKF-Based Real-Time Prediction of Wildfire Propagation
Wildfires are very often difficult to prevent and represent a grave risk; real-time prediction of fire propagation has a vital significance for setting up preventive measures and formulating emergency response plans. However, due to the limitation of computer computation capability, changing of the fire environment and model errors caused by the deficiency of the existed fire simulation tool, current fire models lack the ability to provide considered the accurate prediction of wildfire propagation faster than real-time. Moreover, in the case of the hindrance of heavy smoke, the satellite-based remote sensing or unmanned aerial vehicle (UAV) is incapable of acquiring precise flame front observation data within a certain level of uncertainties. Data assimilation (DA) is extensively functioned in complex predicting problems. And the Ensemble Kalman Filter (EnKF) is one of the best ways of solving large-scale and nonlinear problems, while the computational time is comparatively less than other DA methods. In the present study, real-time prediction of wildfire propagation method is presented by connecting EnKF method and the FARSITE fire area simulator, for updating the dynamically evolving fireline position of a spreading wildfire. The results show that the wildfires forecast are improved with the proposed data-driven methodology than with the stand-alone FARSITE model and successively responsible for an adjusted prediction of the wildfire spread.
EnKF-Based Real-Time Prediction of Wildfire Propagation
Wildfires are very often difficult to prevent and represent a grave risk; real-time prediction of fire propagation has a vital significance for setting up preventive measures and formulating emergency response plans. However, due to the limitation of computer computation capability, changing of the fire environment and model errors caused by the deficiency of the existed fire simulation tool, current fire models lack the ability to provide considered the accurate prediction of wildfire propagation faster than real-time. Moreover, in the case of the hindrance of heavy smoke, the satellite-based remote sensing or unmanned aerial vehicle (UAV) is incapable of acquiring precise flame front observation data within a certain level of uncertainties. Data assimilation (DA) is extensively functioned in complex predicting problems. And the Ensemble Kalman Filter (EnKF) is one of the best ways of solving large-scale and nonlinear problems, while the computational time is comparatively less than other DA methods. In the present study, real-time prediction of wildfire propagation method is presented by connecting EnKF method and the FARSITE fire area simulator, for updating the dynamically evolving fireline position of a spreading wildfire. The results show that the wildfires forecast are improved with the proposed data-driven methodology than with the stand-alone FARSITE model and successively responsible for an adjusted prediction of the wildfire spread.
EnKF-Based Real-Time Prediction of Wildfire Propagation
Wu, Guan-Yuan (editor) / Tsai, Kuang-Chung (editor) / Chow, W. K. (editor) / Zhou, Tengjiao (author) / Ji, Jie (author) / Jiang, Yong (author) / Ding, Long (author)
Asia-Oceania Symposium on Fire Science and Technology ; 2018 ; Taipei, Taiwan
The Proceedings of 11th Asia-Oceania Symposium on Fire Science and Technology ; Chapter: 52 ; 713-724
2020-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
Real-time prediction , Wildfire , Fireline position , Ensemble Kalman filter , State estimation Engineering , Fire Science, Hazard Control, Building Safety , Quality Control, Reliability, Safety and Risk , Engineering Thermodynamics, Heat and Mass Transfer , Building Materials , Renewable and Green Energy
EnKF-Based Real-Time Prediction of Wildfire Propagation
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