Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1667-2024
https://doi.org/10.5194/gmd-17-1667-2024
Development and technical paper
 | 
26 Feb 2024
Development and technical paper |  | 26 Feb 2024

High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning

Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen

Data sets

Data HTC-NN Ferdinand Briegel https://doi.org/10.5281/zenodo.7974307

Model code and software

Code HTC-NN Ferdinand Briegel https://doi.org/10.5281/zenodo.7974472

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Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.