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A visualized soundscape prediction model for design processes in urban parks

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  • Building Thermal, Lighting, and Acoustics Modeling
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Abstract

Existing soundscape prediction model application conditions and calculation processes are very complex, and difficult to combine with design processes. Therefore, in this study, a visual soundscape prediction model is constructed for sound pressure level (SPL) prediction, sound source prediction, and soundscape evaluation prediction by inputting the elements of urban design under the meshing method (grid 50 m) into a machine learning Gaussian mixture model (GMM). Taking three typical urban parks as examples, the soundwalk method is used to collect subjective perception information on-site to verify prediction accuracy. The results show that by applying geographic information data, including the minimum distance values from predicted points to roads, entrances and exits, and internal nodes, the SPL can be predicted. When the accuracy rate is stable within a 3 dBA error range, the prediction accuracy rate is 67.7%. The visual perception information added is used to quickly predict the sound source type, and the output is the visualized distribution of the natural, human, and mechanical sound perception with a 77.4% accuracy rate. Finally, combining geographic, visual and sound data to predict soundscape evaluation, the model’s output are good, medium, and poor categories for each descriptor of the soundscape evaluation, and the prediction results are visualized in three colours with a 74.2% accuracy rate. The convenient soundscape prediction model proposed in this paper can be applied to design practice. By adjusting park design elements, the distribution results can be compared, and an optimal design scheme from the soundscape perspective can be obtained. Combined with the model output, this study compares the model simulation results after adjusting the design elements and the original, and proposes targeted optimisation strategies for urban park soundscape.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (No. 52178070, No. 51878210, No. 51678180, and No. 51608147), the Open Projects Fund of Key Laboratory of Ecology and Energy-saving Study of Dense Habitat (Tongji University), Ministry of Education (No. 2020030103), the Ministry of Science and Technology of China (No. G2021179030L), and the Natural Science Foundation of Heilongjiang Province (YQ2019E022).

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Authors and Affiliations

  1. Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin, China

    Ran Yue, Qi Meng, Da Yang, Yue Wu, Fangfang Liu & Wei Yan

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  1. Ran Yue
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  2. Qi Meng
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  3. Da Yang
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  4. Yue Wu
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  5. Fangfang Liu
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  6. Wei Yan
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Contributions

Ran Yue: conceptualization, methodology, validation, software, resources, data curation, writing—original draft, project administration, funding acquisition. Qi Meng: conceptualization, methodology, resources, data curation, writing—review & editing, project administration, investigation, funding acquisition. Da Yang: conceptualization, software, data curation, writing—review & editing, project administration, funding acquisition. Yue Wu: writing—review & editing, investigation. Fangfang Liu: conceptualization, writing—review & editing, funding acquisition. Wei Yan: writing—review & editing. All authors read and approved the final manuscript.

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Correspondence to Qi Meng.

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The authors have no competing interests to declare that are relevant to the content of this article.

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Yue, R., Meng, Q., Yang, D. et al. A visualized soundscape prediction model for design processes in urban parks. Build. Simul. 16, 337–356 (2023). https://doi.org/10.1007/s12273-022-0955-3

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  • DOI: https://doi.org/10.1007/s12273-022-0955-3

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