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Research on the dual estimation method of fire state-fuel parameters for dynamic prediction of wildfires
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Xiaolong Luo2, Zheng Wang1, Hao Wu2, Jie Ji1, Long Ding1
Fire Science and Technology | 2026, 45(5) : 132 - 142
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Fire Science and Technology | 2026, 45(5): 132-142
• Forest fire control •
Research on the dual estimation method of fire state-fuel parameters for dynamic prediction of wildfires
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Xiaolong Luo2, Zheng Wang1, Hao Wu2, Jie Ji1, Long Ding1
Affiliations
  • 1State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei Anhui 230026, China
  • 2Institute of Advanced Technology, University of Science and Technology of China, Hefei Anhui 230031, China
Published: 2026-05-15 doi: 10.20168/j.1009-0029.2026.05.0132.11
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Since the fireline state and input parameters of wildfire spread prediction models inevitably contain errors and uncertainties, there are usually large deviations between wildfire spread predictions and actual spread results. The Kalman filter method based on data assimilation has the advantage of dynamic estimation in reducing model state errors. In this paper, we propose a dual estimation method for fireline position and fuel parameters based on Multi-Layer Perceptron (MLP) neural network, combined with the Ensemble Transform Kalman Filter (ETKF) algorithm. This method utilizes fireline position data from two consecutive observations and employs ETKF and MLP algorithms to estimate fireline position state and fuel parameters respectively under different prediction periods and fuel adjustment factor resolutions, aiming to reduce the uncertainties in both model state and model parameters. We validated the dual estimation method using a 10 000 m2 prescribed burning experiment and historical wildfire cases. The results demonstrate that the dual estimation algorithm can effectively reduce errors in both fireline state and model parameters, with the predicted fireline positions being to colser actual observations compared to single estimation methods. The prediction period significantly influences the performance of the dual estimation method in reducing fire spread model errors. The longer the prediction period, the greater the improvement in fireline position prediction accuracy compared to single estimation methods. In historical wildfire cases, the dual estimation algorithm shows good overall performance in fireline position prediction, validating the predictive capability of our algorithm in practical scenarios.

wildfire spread prediction  /  fuel adjustment factor  /  parameter estimation  /  ensemble transform Kalman filter algorithm  /  FARSITE
罗肖龙, 王正, 吴昊, 纪杰, 丁龙. 用于森林火灾动态预测的火场状态-燃料参数双估计方法研究. 消防科学与技术, 2026 , 45 (5) : 132 -142 . DOI: 10.20168/j.1009-0029.2026.05.0132.11
Xiaolong Luo, Zheng Wang, Hao Wu, Jie Ji, Long Ding. Research on the dual estimation method of fire state-fuel parameters for dynamic prediction of wildfires[J]. Fire Science and Technology, 2026 , 45 (5) : 132 -142 . DOI: 10.20168/j.1009-0029.2026.05.0132.11
Year 2026 volume 45 Issue 5
12
6
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Article Info
doi: 10.20168/j.1009-0029.2026.05.0132.11
  • Receive Date:2025-02-16
  • Online Date:2026-10-22
  • Published:2026-05-15
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  • Received:2025-02-16
  • Revised:2025-05-02
Affiliations
    1State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei Anhui 230026, China
    2Institute of Advanced Technology, University of Science and Technology of China, Hefei Anhui 230031, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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