PDF
Full
Outline
In order to address the challenges of unclear spatial concentration distribution and uncertain future evolution in high-pressure large-diameter gas pipeline leakage scenarios, a predictive model for gas leakage dispersion was proposed by integrating machine learning-based dimensionality reduction and time series forecasting methods. Firstly, a multi-condition dataset of gas leakage concentration fields was generated using computational fluid dynamics simulations. Subsequently, the dimensionality reduction module and time series forecasting module of the predictive model were separately optimized and trained using this dataset. Finally, the model's predictive accuracy was evaluated on an independent test set, and the prediction errors under various forecast horizons were analyzed. The results show that the model achieves a mean absolute error of 0.000 5 and a mean absolute percentage error (mAPE) of 6.82% on the test set, with the mAPE remaining below 14% across different prediction time steps.
PDF
62
8
| 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
010-62199257
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
