Machine learning-based urban noise appropriateness evaluation method and driving factor analysis.

ABSTRACT: 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 significan

SUBMITTER: Teng J 

PROVIDER: S-EPMC11666067 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature