The Impact of High-density Urban Wind Environments on the Distribution of COVID-19 Based on Machine Learning: A Case Study of Macau (Training set for machine learning)
Description
This is the corresponding machine learning training set for the paper "The Impact of High-density Urban Wind Environments on the Distribution of COVID-19 Based on Machine Learning: A Case Study of Macau". For details, please refer to our published research papers. The COVID-19 epidemic has become a global challenge, and the urban wind environment, as an important part of urban spaces, may play a key role in the spread of the virus. Therefore, an in-depth understanding of the impact of urban wind environments on the spread of COVID-19 is of great significance for formulating effective prevention and control strategies. This paper adopts the conditional generative confrontation network (CGAN) method, uses simulated urban wind envi-ronment data and COVID-19 distribution data for machine training, and trains a model to predict the distribution probability of COVID-19 under different wind environments. Through the appli-cation of this model, the relationship between the urban wind environment and the spread of COVID-19 can be studied in depth. This study found that: (1) there are significant differences in the different types of wind environments and COVID-19 and areas with high building density are more susceptible to COVID-19 hotspots; (2) the distribution of COVID-19 hotspots in building complexes and the characteristics of the building itself are correlated; and (3) similarly, the building area in-fluences the spread of COVID-19. In response to long COVID or residential area planning in the post-epidemic era, three principles can be considered for high-density cities such as Macau: building houses on the northeast side of the mountain; making residential building layouts of "strip" or "rectangular" design; and ensuring that the long side of the building faces southeast (the windward side). (4) It is recommended that the overall wind speed around the building be greater than 2.91 m/s, and the optimal wind speed is between 4.85 and 8.73 m/s. This finding provides valuable in-formation for urban planning and public health departments to help formulate more effective ep-idemic prevention and control strategies. This study uses machine-learning methods to reveal the impact of urban wind environments on the distribution of COVID-19 and provides important in-sights into urban planning and public health strategy development.
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Institutions
- Macau University of Science and Technology