A cross-regional machine learning transfer prediction framework for heat risk based on environmental characteristics of mature new towns

Heat risk assessment for planned new towns is challenging because actual thermal environments, population activities, and built environment conditions are not yet fully formed. This study examines whether heat risk relationships identified in a mature new town environment can be translated into planning stage variables to support ex-ante risk screening before development is completed. Using Bundang-Pangyo, South Korea, as the mature reference area, composite heat risk was characterized through heat hazard, exposure, and built environment vulnerability. Spatial analysis revealed substantial within city heterogeneity, indicating that heat risk relationships were not spatially uniform across the reference area. Planning stage land use, road, and building development characteristics were then used as common intermediary variables for cross-regional machine learning prediction. The results show that conventional land use and road characteristics captured meaningful variation in heat risk, while the inclusion of additional building development and three-dimensional planning information substantially improved predictive performance. Application to Goyang Changneung and Namyangju Wangsuk produced clearly differentiated planning stage risk profiles, demonstrating that the transferred framework can distinguish spatial variations associated with different planned urban configurations. SHAP analysis indicated that planned cooling balance, planned normalized building coverage ratio, and planned impervious surface ratio were among the more prominent variables associated with the transferred predictions. These findings emphasize the joint importance of ecological cooling capacity and development intensity for planning stage heat risk screening. Methodologically, the study provides a way to translate mature city heat risk knowledge into variables available before construction, extending urban heat risk assessment from post development diagnosis toward planning stage comparative screening.

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Publication Details

Journal
Frontiers in Environmental Science
Published
2026-10-28
DOI
https://doi.org/10.3389/fenvs.2026.2012398
Primary Topic
Urban Heat Island Mitigation
Type
article
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article

A cross-regional machine learning transfer prediction framework for heat risk based on environmental characteristics of mature new towns

Hwanyong Kim, Juncheng Zeng, Dongyoon Lee
6 links
Frontiers in Environmental Science
Urban Heat Island Mitigation
article

A cross-regional machine learning transfer prediction framework for heat risk based on environmental characteristics of mature new towns

Hwanyong Kim, Juncheng Zeng, Dongyoon Lee
article en

Abstract

Heat risk assessment for planned new towns is challenging because actual thermal environments, population activities, and built environment conditions are not yet fully formed. This study examines whether heat risk relationships identified in a mature new town environment can be translated into planning stage variables to support ex-ante risk screening before development is completed. Using Bundang-Pangyo, South Korea, as the mature reference area, composite heat risk was characterized through heat hazard, exposure, and built environment vulnerability. Spatial analysis revealed substantial within city heterogeneity, indicating that heat risk relationships were not spatially uniform across the reference area. Planning stage land use, road, and building development characteristics were then used as common intermediary variables for cross-regional machine learning prediction. The results show that conventional land use and road characteristics captured meaningful variation in heat risk, while the inclusion of additional building development and three-dimensional planning information substantially improved predictive performance. Application to Goyang Changneung and Namyangju Wangsuk produced clearly differentiated planning stage risk profiles, demonstrating that the transferred framework can distinguish spatial variations associated with different planned urban configurations. SHAP analysis indicated that planned cooling balance, planned normalized building coverage ratio, and planned impervious surface ratio were among the more prominent variables associated with the transferred predictions. These findings emphasize the joint importance of ecological cooling capacity and development intensity for planning stage heat risk screening. Methodologically, the study provides a way to translate mature city heat risk knowledge into variables available before construction, extending urban heat risk assessment from post development diagnosis toward planning stage comparative screening.

Frontiers in Environmental ScienceVol. 14
Hanyang University (KR)
Sustainable cities and communities, Industry, innovation and infrastructure, Climate action
Openalex Percentile: Top 22%
Urban Heat Island Mitigation
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A cross-regional machine learning transfer prediction framework for heat risk based on environmental characteristics of mature new towns — Hwanyong Kim, Juncheng Zeng, et al. · Frontiers in Environmental Science (2026) | TGRS Research Map | TGRS