Evaluating the effect of building patterns on urban flooding based on a
boosted regression tree: a case study of Beijing, China
Abstract
Rapid urbanization and global climate change are likely to exacerbate
urban flooding intensity, frequency, and uncertainty. Thus, it is
fundamental and crucial to investigate the dominant influencing factors
for the mitigation of urban flooding. However, the influence of building
patterns on urban flooding remains limited. Taking Beijing, a typical
megacity, as a case study area, we quantified the importance of building
patterns and their interaction effect at the subwatershed scale using
the boosted regression tree (BRT) and geographical detector model
(GeoD). The results indicated that (1) the landscape shape index, slope,
green space ratio and waterbody ratio were the most important
influencing factors determining urban flooding, with a total relative
contribution of 67.23%, (2) building metrics had a certain impact on
urban flooding, and the sum of the relative contribution can reach
21.03%, (3) with urban flooding density, the landscape shape index,
slope, and green space ratio exhibited a combination of negative and
positive correlation, and (4) an enhancement effect existed between
building metrics, especially the building congestion degree and building
density. These findings provide quantitative insights that rational
urban morphology planning can improve stormwater management and promote
urban sustainability in megacities.