Using XGBoost and SHAP on simulated Tehran blocks, the study ranks 30 urban morphology parameters and finds building shape, window-to-wall ratio, and commercial floor share dominate energy demand, while neighbor heights and distances drive cooling and solar access.
Fast estimation of airflow distribution in an urban model using generative adversarial networks with limited sensing data☆,
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Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates
Using XGBoost and SHAP on simulated Tehran blocks, the study ranks 30 urban morphology parameters and finds building shape, window-to-wall ratio, and commercial floor share dominate energy demand, while neighbor heights and distances drive cooling and solar access.