Full
Outline
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.
5
2
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
