A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines
<p>This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On t...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-02-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/737/2025/amt-18-737-2025.pdf |
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| author | X. Xiong J. Chen Y. Zhang X. Chen Y. Zhang X. Ye |
| author_facet | X. Xiong J. Chen Y. Zhang X. Chen Y. Zhang X. Ye |
| author_sort | X. Xiong |
| collection | DOAJ |
| description | <p>This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On the one hand, based on the special structure in railway assembly, the physical constraint model of the railway electrical catenary supports and anemometers was experimentally established. The performance of the physical model in the wind field was simulated based on FLUENT software, and the environmental change characteristics of the anemometer in the railway area were analyzed. On the other hand, to solve the constrained error mapping expression under different wind conditions, a data-driven model of hyperparameter optimization (BO-XGBoost) is introduced to perform error compensation on physical relationships. Through the PD-BX method, the RMSE of the railway anemometer was reduced by 2.497 from 2.790 to 0.293, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system.</p> |
| format | Article |
| id | doaj-art-a95f71afa1a340b7b105cdfc57c1369c |
| institution | Kabale University |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-a95f71afa1a340b7b105cdfc57c1369c2025-02-10T07:40:10ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-02-011873774810.5194/amt-18-737-2025A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway linesX. Xiong0J. Chen1Y. Zhang2X. Chen3Y. Zhang4X. Ye5Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaInformation and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaInformation and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaInformation and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China<p>This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On the one hand, based on the special structure in railway assembly, the physical constraint model of the railway electrical catenary supports and anemometers was experimentally established. The performance of the physical model in the wind field was simulated based on FLUENT software, and the environmental change characteristics of the anemometer in the railway area were analyzed. On the other hand, to solve the constrained error mapping expression under different wind conditions, a data-driven model of hyperparameter optimization (BO-XGBoost) is introduced to perform error compensation on physical relationships. Through the PD-BX method, the RMSE of the railway anemometer was reduced by 2.497 from 2.790 to 0.293, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system.</p>https://amt.copernicus.org/articles/18/737/2025/amt-18-737-2025.pdf |
| spellingShingle | X. Xiong J. Chen Y. Zhang X. Chen Y. Zhang X. Ye A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines Atmospheric Measurement Techniques |
| title | A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines |
| title_full | A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines |
| title_fullStr | A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines |
| title_full_unstemmed | A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines |
| title_short | A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines |
| title_sort | quality control method based on physical constraints and data driven collaborative artificial intelligence for wind observations along high speed railway lines |
| url | https://amt.copernicus.org/articles/18/737/2025/amt-18-737-2025.pdf |
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