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It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F1-score greater than 80% and can be combined with any wind rose statistic.<\/jats:p>","DOI":"10.3390\/make6010006","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T09:47:32Z","timestamp":1704361652000},"page":"98-125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4513-5035","authenticated-orcid":false,"given":"Jennifer","family":"Werner","sequence":"first","affiliation":[{"name":"Optimization Department, Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3837-9461","authenticated-orcid":false,"given":"Dimitri","family":"Nowak","sequence":"additional","affiliation":[{"name":"Optimization Department, Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2375-1328","authenticated-orcid":false,"given":"Franziska","family":"Hunger","sequence":"additional","affiliation":[{"name":"Computational Engineering and Design Department, Fraunhofer-Chalmers Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4472-5700","authenticated-orcid":false,"given":"Tomas","family":"Johnson","sequence":"additional","affiliation":[{"name":"Computational Engineering and Design Department, Fraunhofer-Chalmers Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0038-3307","authenticated-orcid":false,"given":"Andreas","family":"Mark","sequence":"additional","affiliation":[{"name":"Computational Engineering and Design Department, Fraunhofer-Chalmers Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3530-2208","authenticated-orcid":false,"given":"Alexander","family":"G\u00f6sta","sequence":"additional","affiliation":[{"name":"Architecture and Spatial Planning, RISE\u2014Research Institutes of Sweden, Drottning Kristinas V\u00e4g 61, SE-114 28 Stockholm, Sweden"},{"name":"Liljewall Arkitekter, Odinsplatsen 1, SE-411 02 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3894-1837","authenticated-orcid":false,"given":"Fredrik","family":"Edelvik","sequence":"additional","affiliation":[{"name":"Computational Engineering and Design Department, Fraunhofer-Chalmers Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"ref_1","unstructured":"Lawson, T.V. 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