Machine learning applications on evaluating the cooling effect of urban blue-green infrastructure: A systematic review

Blue-green infrastructure (BGI) is an important nature-based solution to mitigate the urban heat island effect. Machine learning (ML) techniques can integrate multi-source data to more quickly and accurately assess the cooling effect of BGI and understand the complex nonlinear cooling mechanism. This study systematically reviews 38 journal articles published from 2012 to today that applied ML to evaluate the cooling effect of BGI covering different scales and types. Studies are categorized according to six criteria: Spatiotemporal Scale; ML algorithm; BGI Type; Cooling Effect Metrics; Input Data and Data Source. Artificial Neural Network and Random Forest are the two mostly-used ML algorithms. BGI types include trees, green roof, green wall, water body, open green space and green patches, among which green patches are the most studied while trees and green walls are the least. The background climate, urban morphology and evaluation methods of BGI cooling effect vary significantly among studies and it is thus difficult to compare and synthesize the cooling efficacy performance: Studies that cover multiple climatic zones with long time series meteorological data (e.g., multi-year observation) would support generalizability but these studies are lacking. BGI land surface coverage are the most frequently-used indicators, whereas 3-Dimensional indicators are much less adopted: less than 20% of studies used 3D building indicators and only 10% used 3D vegetation indicators. More than half of the studies’ labeled data source is from remote sensing, while fewer used air temperature and thermal comfort data that could more accurately reflect local and microscale climates. The review underscores the need for more comprehensive BGI studies, covering larger spatiotemporal ranges and more representative datasets to improve the effectiveness and generalizability of BGI-cooling ML models.