Machine Learning-Based Urban Renovation Design for Improving Wind Environment: A Case Study in Xi’an, China

Abstract

The high-density urban form and building arrangement of modern cities have contributed to numerous environmental problems. The calm wind area caused by inappropriate building arrangements results in pollutant accumulation. To realize a practical design and improve urban microclimate, we investigated the spatial relationship between roads, buildings, and open space using the machine learning technique. First, region growing and k-means clustering were employed to identify roads and buildings. Based on the image masking program, we selected training areas according to the land use map. Second, we used the multiple-point statistics technique to create new urban fabric images. Viewing the training image as a prior model, our program constantly reproduced morphological structures in the target area. We intensified the similarity with training areas and enriched the variability among generated images. Third, Hausdorff distance and multidimensional scaling were applied to achieve a quality examination. The proposed method was performed to fulfill an urban renovation design in Xi’an, China. Based on the historical record, we applied computational fluid dynamics to simulate air circulation and ventilation. The results indicate that the size of calm wind area is reduced. The wind environment is significantly improved due to the rising wind speed.