Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model
Abstract
To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~∘C during the day and 2.6~∘C at night, while UTCI varies by up to 7.9~∘C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.
Related
- A Hybrid Framework for Air Pollution Forecasting using DSES, DLA, Self-Attention Transformer, and BiGRU with Bayesian Optimization
- Input-data perturbation in groundwater level prediction: a controlled experiment on accuracy–reliability trade-offs among data augmentation and Bayesian-type uncertainty estimation methods
- Modelling spatial trade-offs in urban sub-centre planning: A prospective scenario framework linking land value capture and residential welfare
- Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
- Reconciliation Through Integration: A Source-Level Bayesian Framework for Developing Measurement-Integrated Methane Inventories
BibTeX
@misc{walter2026analysis,
title = {{Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model}},
author = {Rebekka Walter and Johanna Gelhaus and David Anton and Henning Wessles and Stephan Weber},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25953},
eprint = {2609.25953},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25953},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model
AU - Rebekka Walter
AU - Johanna Gelhaus
AU - David Anton
AU - Henning Wessles
AU - Stephan Weber
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.25953
UR - https://arxiv.org/abs/2609.25953
AB - To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~^∘C during the day and 2.6~^∘C at night, while UTCI varies by up to 7.9~^∘C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.
ER -