Machine Learning Application in Investigating Cooling Effect of Urban Blue–Green Infrastructure: A Systematic Review

Blue–green infrastructure (BGI) is an important nature-based solution for mitigating urban overheating, and machine learning (ML) techniques offer strong potential in capturing the complex, nonlinear relationships between influencing factors and BGI cooling effects. This study presents a systematic review of 54 journal articles published between 2015 and 2025 that applied ML to assess the cooling performance of BGI. We classified BGI into seven types across three spatial scales, evaluated the metrics and indicators used to characterize the BGI cooling effects, and examined the ML algorithms, model performance, and data sources adopted in reviewed studies. We found that 2D BGI morphology metrics and BGI physical and biological metrics are most frequently used as model inputs, whereas 3D morphology metrics are used less. Surface temperature-related indicators dominate model outputs, while air temperature and human heat stress indicators, which better reflect heat health hazards, remain underutilized. In terms of methodology, ML applications are primarily based on artificial neural networks for temporal regression and tree ensemble algorithms for spatial regression. Building on these findings, we identify key research gaps and advocate that future research should develop comprehensive multi-source and multi-scale BGI databases, improve model transparency and generalizability, and integrate energy-balance information into BGI-ML studies. Finally, we propose a systematic BGI-ML modeling framework to guide future research in this field.