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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

  • Research Article
  • Advances in Modeling and Simulation Tools
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Abstract

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions. Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations. Deep learning, particularly generative adversarial networks (GAN), has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow. However, existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images. This study proposes a novel approach termed SFGAN, which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction. In the spatial domain, Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges, which are used as the embedded spatial characteristics. Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics. These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase, thereby leading to enhanced predictions. Experimental results demonstrate that SFGAN clearly improves wind flow prediction, reducing Wind_MAE, Wind_RMSE and the Fréchet Inception Distance (FID) score by 5.35%, 6.52% and 12.30%, compared to the previous best method, respectively. We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow. SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings. The enhanced predictions provide a better understanding of performance variability, bringing insights at the early design stage to improve pedestrian wind comfort. The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.

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References

  • Balasubramanian A, Dhanasekaran H, Raghu B, et al. (2022). MRI super-resolution using generative adversarial network and discrete wavelet transform. In: Proceedings of 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS).

  • Baskaran A, Kashef A (1996). Investigation of air flow around buildings using computational fluid dynamics techniques. Engineering Structures, 18: 861–875.

    Article  Google Scholar 

  • Calzolari G, Liu W (2021). Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review. Building and Environment, 206: 108315.

    Article  Google Scholar 

  • Cao Q, Luan Q, Liu Y, et al. (2021). The effects of 2D and 3D building morphology on urban environments: A multi-scale analysis in the Beijing metropolitan region. Building and Environment, 192: 107635.

    Article  Google Scholar 

  • Chen Y, Li G, Jin C, et al. (2021). SSD-GAN: measuring the realness in the spatial and spectral domains. Proceedings of the AAAI Conference on Artificial Intelligence, 35: 1105–1112.

    Article  Google Scholar 

  • Duering S, Chronic A, Koenig R. (2020). Optimizing Urban Systems: Integrated optimization of spatial configurations. In: Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design.

  • Frank J, Eisenhofer T, Schönherr L, et al. (2020). Leveraging frequency analysis for deep fake image recognition. In: Proceedings of the 37th International Conference on Machine Learning.

  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. (2020). Generative adversarial networks. Communications of the ACM, 63: 139–144.

    Article  Google Scholar 

  • He Y, Liu Z, Ng E (2022). Parametrization of irregularity of urban morphologies for designing better pedestrian wind environment in high-density cities–A wind tunnel study. Building and Environment, 226: 109692.

    Article  Google Scholar 

  • Heideman M, Johnson D, Burrus C (1984). Gauss and the history of the fast Fourier transform. IEEE ASSP Magazine, 1(4): 14–21.

    Article  Google Scholar 

  • Heusel M, Ramsauer H, Unterthiner T, et al. (2017). GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems.

  • Higgins S, Stathopoulos T (2021). Application of artificial intelligence to urban wind energy. Building and Environment, 197: 107848.

    Article  Google Scholar 

  • Huang C, Zhang G, Yao J, et al. (2022). Accelerated environmental performance-driven urban design with generative adversarial network. Building and Environment, 224: 109575.

    Article  Google Scholar 

  • Ignatius M, Wong NH, Jusuf SK (2015). Urban microclimate analysis with consideration of local ambient temperature, external heat gain, urban ventilation, and outdoor thermal comfort in the tropics. Sustainable Cities and Society, 19: 121–135.

    Article  Google Scholar 

  • Iqbal QMZ, Chan ALS (2016). Pedestrian level wind environment assessment around group of high-rise cross-shaped buildings: Effect of building shape, separation and orientation. Building and Environment, 101: 45–63.

    Article  Google Scholar 

  • Isola P, Zhu J, Zhou T, et al. (2017). Image-to-image translation with conditional adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  • Javanroodi K, Nik VM, Giometto MG, et al. (2022). Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology. Science of the Total Environment, 829: 154223.

    Article  Google Scholar 

  • Jiang Y, Alexander D, Jenkins H, et al. (2003). Natural ventilation in buildings: Measurement in a wind tunnel and numerical simulation with large-eddy simulation. Journal of Wind Engineering and Industrial Aerodynamics, 91: 331–353.

    Article  Google Scholar 

  • Jiang L, Dai B, Wu W, et al. (2021). Focal frequency loss for image reconstruction and synthesis. In: Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV).

  • Karras T, Laine S, Aila T (2019). A style-based generator architecture for generative adversarial networks. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Kastner P, Dogan T (2022). Eddy3D: A toolkit for decoupled outdoor thermal comfort simulations in urban areas. Building and Environment, 212: 108639.

    Article  Google Scholar 

  • Khayatkhoei M, Elgammal A (2022). Spatial frequency bias in convolutional generative adversarial networks. Proceedings of the AAAI Conference on Artificial Intelligence, 36: 7152–7159.

    Article  Google Scholar 

  • Lawson TV (1978). The wind content of the built environment. Journal of Wind Engineering and Industrial Aerodynamics, 3: 93–105.

    Article  Google Scholar 

  • Long Y (2016). Redefining Chinese city system with emerging new data. Applied Geography, 75: 36–48.

    Article  Google Scholar 

  • Mallat S (1999). A Wavelet Tour of Signal Processing, 2nd edn. San Diego, CA, USA: Academic Press.

    Google Scholar 

  • Mittal H, Sharma A, Gairola A (2018). A review on the study of urban wind at the pedestrian level around buildings. Journal of Building Engineering, 18: 154–163.

    Article  Google Scholar 

  • MOHURD (2019). GB/T50378-2019. Assessment Standard for Green Building. Ministry of Housing and Urban–Rural Development of China. (in Chinese)

  • Mokhtar S, Sojka A, Davila CC. (2020). Conditional generative adversarial networks for pedestrian wind flow approximation. In: Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design.

  • Mokhtar S, Beveridge M, Cao Y, et al. (2021). Pedestrian wind factor estimation in complex urban environments. In: Proceedings of the 13th Asian Conference on Machine Learning.

  • Moonen P, Defraeye T, Dorer V, et al. (2012). Urban Physics: Effect of the micro-climate on comfort, health and energy demand. Frontiers of Architectural Research, 1: 197–228.

    Article  Google Scholar 

  • Norouziasas A, Ha PP, Ahmadi M, et al. (2022). Evaluation of urban form influence on pedestrians’ wind comfort. Building and Environment, 224: 109522.

    Article  Google Scholar 

  • Park T, Efros AA, Zhang R, et al. (2020). Contrastive learning for unpaired image-to-image translation. In: Proceedings of European Conference on Computer Vision.

  • Seddik H, Braiek EB (2012). Efficient noise removing based optimized smart dynamic Gaussian filter. International Journal of Computer Applications, 51(5): 1–13.

    Article  Google Scholar 

  • Szegedy C, Vanhoucke V, Ioffe S, et al. (2016). Rethinking the inception architecture for computer vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  • Tan C, Zhong X (2022).A rapid wind velocity prediction method in built environment based on CycleGAN model. In: Proceedings of the International Conference on Computational Design and Robotic Fabrication.

  • Toparlar Y, Blocken B, Maiheu B, et al. (2017). A review on the CFD analysis of urban microclimate. Renewable and Sustainable Energy Reviews, 80: 1613–1640.

    Article  Google Scholar 

  • Tsichritzis L, Nikolopoulou M (2019). The effect of building height and façade area ratio on pedestrian wind comfort of London. Journal of Wind Engineering and Industrial Aerodynamics, 191: 63–75.

    Article  Google Scholar 

  • Wang X, Li Y (2016). Predicting urban heat island circulation using CFD. Building and Environment, 99: 82–97.

    Article  Google Scholar 

  • Wu Y, Zhan Q, Quan SJ, et al. (2021). A surrogate-assisted optimization framework for microclimate-sensitive urban design practice. Building and Environment, 195: 107661.

    Article  Google Scholar 

  • Yang J, Shi B, Shi Y, et al. (2020). Air pollution dispersal in high density urban areas: Research on the triadic relation of wind, air pollution, and urban form. Sustainable Cities and Society, 54: 101941.

    Article  Google Scholar 

  • Yang X, Zhao J, Wei Z, et al. (2022). SAR-to-optical image translation based on improved CGAN. Pattern Recognition, 121: 108208.

    Article  Google Scholar 

  • Yang G, Song C, Yang Z, et al. (2023). Bubble detection in photoresist with small samples based on GAN augmentations and modified YOLO. Engineering Applications of Artificial Intelligence, 123: 106224.

    Article  Google Scholar 

  • Yi R, Liu Y, Lai Y, et al. (2019). APDrawingGAN: Generating artistic portrait drawings from face photos with hierarchical GANs. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Zaki SA, Azid NS, Shahidan MF, et al. (2020). Analysis of urban morphological effect on the microclimate of the urban residential area of Kampung Baru in Kuala Lumpur using a geospatial approach. Sustainability, 12: 7301.

    Article  Google Scholar 

  • Zhang X, Karaman S, Chang SF (2019). Detecting and simulating artifacts in GAN fake images. In: Proceedings of 2019 IEEE International Workshop on Information Forensics and Security (WIFS).

  • Zhang Y, Liu T, Zhao B, et al. (2023). SFA-GAN: structure-frequency-aware generative adversarial network for underwater image enhancement. Signal, Image and Video Processing, 17: 3647–3655.

    Article  Google Scholar 

  • Zhong J, Liu J, Zhao Y, et al. (2022). Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment. Architectural Intelligence, 1: 5.

    Article  Google Scholar 

  • Zhu J, Park T, Isola P, et al. (2017a). Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of IEEE International Conference on Computer Vision (ICCV).

  • Zhu J-Y, Zhang R, Pathak D, et al. (2017b). Toward multimodal image-to-image translation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17).

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Acknowledgements

This work was financially supported by the Beijing Municipal Natural Science Foundation [No. 4232021]; the National Natural Science Foundation of China [No. 62271036, No. 62271035, No. 62101022]; the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture [No. JDYC20220818]; theYoung teachers research ability enhancement program of Beijing University of Civil Engineering and Architecture [No. X21083].

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Authors and Affiliations

  1. School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Pengyue Wang & Shimeng Hao

  2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, 100044, China

    Pengyue Wang, Maozu Guo, Yingeng Cao & Xiaoping Zhou

  3. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Maozu Guo, Yingeng Cao & Xiaoping Zhou

  4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Lingling Zhao

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  1. Pengyue Wang
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  2. Maozu Guo
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  3. Yingeng Cao
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  4. Shimeng Hao
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  5. Xiaoping Zhou
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  6. Lingling Zhao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Pengyue Wang, Yingeng Cao, Lingling Zhao and Shimeng Hao. The first draft of the manuscript was written by Pengyue Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xiaoping Zhou or Lingling Zhao.

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Wang, P., Guo, M., Cao, Y. et al. Pedestrian wind flow prediction using spatial-frequency generative adversarial network. Build. Simul. 17, 319–334 (2024). https://doi.org/10.1007/s12273-023-1071-8

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  • DOI: https://doi.org/10.1007/s12273-023-1071-8

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