Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks
In addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution source inversion method based on small samples. By in...
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MDPI AG
2025-04-01
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| author | Tiancheng Ye Mengtao Han |
| author_facet | Tiancheng Ye Mengtao Han |
| author_sort | Tiancheng Ye |
| collection | DOAJ |
| description | In addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution source inversion method based on small samples. By integrating computational fluid dynamics simulation data and deep learning techniques, a spatial pollution source identification model suitable for limited-sample conditions was constructed. In a benchmark scenario, the optimized model achieved a localization of 82.3% weighted accuracy within a prediction radius of 1 m, and the corresponding normalized error of the detected area was of less than 0.26%. In cross-scenario verification, the localization accuracy within a 1 m radius increased to 100%, and the corresponding predicted Euclidean distance error decreased by 21.43%. By using the optimal cutting ratio (α = 0.25) and a rotation-enhanced dataset (θ = 10°, n = 36), the model reduced the cross-space sample requirement to 1/5 of that of the benchmark scenario while ensuring the accuracy of spatial representation. The research findings provide an efficient and reliable deep learning solution for the localization of pollution sources in complex spaces. |
| format | Article |
| id | doaj-art-7246afff6950426b954ac52cc4580366 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-7246afff6950426b954ac52cc45803662025-08-20T03:14:15ZengMDPI AGBuildings2075-53092025-04-01158124410.3390/buildings15081244Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural NetworksTiancheng Ye0Mengtao Han1School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaIn addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution source inversion method based on small samples. By integrating computational fluid dynamics simulation data and deep learning techniques, a spatial pollution source identification model suitable for limited-sample conditions was constructed. In a benchmark scenario, the optimized model achieved a localization of 82.3% weighted accuracy within a prediction radius of 1 m, and the corresponding normalized error of the detected area was of less than 0.26%. In cross-scenario verification, the localization accuracy within a 1 m radius increased to 100%, and the corresponding predicted Euclidean distance error decreased by 21.43%. By using the optimal cutting ratio (α = 0.25) and a rotation-enhanced dataset (θ = 10°, n = 36), the model reduced the cross-space sample requirement to 1/5 of that of the benchmark scenario while ensuring the accuracy of spatial representation. The research findings provide an efficient and reliable deep learning solution for the localization of pollution sources in complex spaces.https://www.mdpi.com/2075-5309/15/8/1244pollution source inversionconvolutional neural network (CNN)deep learningsmall samplespatial localization accuracy |
| spellingShingle | Tiancheng Ye Mengtao Han Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks Buildings pollution source inversion convolutional neural network (CNN) deep learning small sample spatial localization accuracy |
| title | Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks |
| title_full | Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks |
| title_fullStr | Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks |
| title_full_unstemmed | Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks |
| title_short | Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks |
| title_sort | indoor air pollution source localization based on small sample training convolutional neural networks |
| topic | pollution source inversion convolutional neural network (CNN) deep learning small sample spatial localization accuracy |
| url | https://www.mdpi.com/2075-5309/15/8/1244 |
| work_keys_str_mv | AT tianchengye indoorairpollutionsourcelocalizationbasedonsmallsampletrainingconvolutionalneuralnetworks AT mengtaohan indoorairpollutionsourcelocalizationbasedonsmallsampletrainingconvolutionalneuralnetworks |