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<div><p>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 algorith...

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Bibliographic Details
Main Author:	Jinlin Teng (17972595) (author)
Other Authors:	Cheng Zhang (70708) (author), Huimin Gong (5949155) (author), Chunqing Liu (424072) (author)
Published:	2024
Subjects:	Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified top three features shap model identifies road network density random forest algorithm population spatial distribution urban spatial planning central urban region noise suitability levels noise mitigation measures investigate noise suitability 38 %, 16 03 %, 28 02 %, 33 urban noise suitability urban noise levels xlink "> 4 xlink "> 3 xlink "> 2 xlink "> 1 nanchang &# 8217 urban environmental management central urban area machine learning techniques machine learning algorithms urban noise evaluation urban noise xlink "> machine learning noise management 31 %, nanchang city key area water bodies varying impacts sustainable development slightly suitable significant impact relatively suitable primarily concentrated performed best moderately suitable great interest ganjiang river feature variables effectively used effective methods east bank different values
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