4.2 Interaction effect between 3D landscape metrics
Although numerous studies conduct multivariate analysis in urban
flooding by using traditional statistical methods (i.e., pearson
correlation, logistic regression, partial least squares, structural
equation model) or machine learning method (i.e., random forest), fewer
studies explore the interaction effect among explanatory factors (Ma et
al., 2022). Urban morphology was complex and high heterogeneous,
especially in the vertical direction. Existing 3D landscape metrics was
still insufficient to analysis the urban canopy. Thus, it was necessary
to explore the interaction effect among 3D landscape metrics. For
example, more and aggregation impervious surfaces tend to rise the land
surface temperature in urban heat island field. However, high-rise
building can mitigate heat island effect through improve surface
roughness and cast more shadows (Sun et al., 2020). How to simultaneous
adjust the impervious surfaces and building height became vital to
alleviate heat island. In our study, we revealed that enhancement effect
exist between 3D building metrics. In particularly, when the 3D fractal
as an given value, it is important to reduce 3D shape index to avoid
urban flooding risks.