Machine learning-based urban noise appropriateness evaluation method and driving factor analysis.
The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed b...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0311571 |
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| author | Jinlin Teng Cheng Zhang Huimin Gong Chunqing Liu |
| author_facet | Jinlin Teng Cheng Zhang Huimin Gong Chunqing Liu |
| author_sort | Jinlin Teng |
| collection | DOAJ |
| description | The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City. 2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures. 3.The weakly suitable noise areas in Nanchang's central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively. 4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability. This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment. |
| format | Article |
| id | doaj-art-19c9848108254f6e90bba9cb2242d0fe |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-19c9848108254f6e90bba9cb2242d0fe2025-01-08T05:32:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031157110.1371/journal.pone.0311571Machine learning-based urban noise appropriateness evaluation method and driving factor analysis.Jinlin TengCheng ZhangHuimin GongChunqing LiuThe evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City. 2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures. 3.The weakly suitable noise areas in Nanchang's central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively. 4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability. This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment.https://doi.org/10.1371/journal.pone.0311571 |
| spellingShingle | Jinlin Teng Cheng Zhang Huimin Gong Chunqing Liu Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. PLoS ONE |
| title | Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. |
| title_full | Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. |
| title_fullStr | Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. |
| title_full_unstemmed | Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. |
| title_short | Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. |
| title_sort | machine learning based urban noise appropriateness evaluation method and driving factor analysis |
| url | https://doi.org/10.1371/journal.pone.0311571 |
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