  The rising air temperature caused by Urban Heat Island (UHI) effect has
become a problem for Singapore, it not only affects the thermal comfort of
outdoor microclimate environment, but also increases the cooling energy
consumption of buildings. As part of a multiscale and multi-physics urban
microclimate model, weather stations were installed at 15 points within kent
ridge campus of National University of Singapore (NUS) and continuously
recorded the microclimate data from February 2019 to May 2019. A Geographical
Information System (GIS) map and 3D model were constructed for extracting urban
morphology parameters such as BDG, PAVE, WALL and HBDG. Through a site survey,
SVF and GnPR were calculated. By using multi-criteria linear regression and
machine learning, this research investigated five regression models for
prediction of outdoor air temperature including linear regression (LR),
k-nearest neighbours (KNN), support vector regression (SVR), decision tree (DT)
and random forests (RF). The analysis of variables by best subsets regression
showed greenery played crucial role in the mitigation of both daytime and
night-time UHI. Pedestrian level wind flow was helpful in heat release in the
daytime. High-rise buildings provided self-shadowing to reduce ambient air
temperature but higher SVF was harmful to heat release in the night-time. For
regression models, RF had the best predictive performance. Average RMSE of RF
was reduced by 4% to 29% compared to linear regression. The learning curve
indicated that the predictive power of LR could not be improved by additional
data provision. In contrast, the downward trend in bias and variance suggested
that RF can benefit from the training of big data. During the deployment of
learning algorithms, RF continued to outperform other learning algorithms.
