{"id":18531,"date":"2024-08-07T09:09:22","date_gmt":"2024-08-07T09:09:22","guid":{"rendered":"https:\/\/uni-freiburg.de\/uni-freiburg-migration\/?p=18531"},"modified":"2024-09-25T16:00:54","modified_gmt":"2024-09-25T14:00:54","slug":"how-cities-can-adapt-to-climate-change-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/uni-freiburg.de\/en\/how-cities-can-adapt-to-climate-change-with-artificial-intelligence\/","title":{"rendered":"How Cities Can Adapt to Climate Change with Artificial Intelligence"},"content":{"rendered":"\n
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Freiburg, 05\/07\/2024<\/em><\/p>\n\n\n\n

The Freiburg project \u2018I4C \u2013 Intelligence for Cities\u2019 has developed locally precise climate models and foundations for planning tools<\/strong><\/p>\n<\/div>\n\n\n\n

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Urban spaces are particularly susceptible to the effects of climate change, such as heat waves, floods, and storms. But which areas of a city are affected, and how can city planners respond? \u2018I4C \u2013 Intelligence for Cities\u2019 is an interdisciplinary project conducted by the University of Freiburg and several Fraunhofer Institutes. The researchers spent the last three and a half years developing models that enable precise and detailed measurements and predictions with the help of artificial intelligence (AI) \u2013 for example on the frequency of thermal stress, but also on the effects of changes in planning, such as planting trees or increasing the population density of a neighbourhood. The project also treated ethical issues in the use of AI and the importance of data protection.<\/p>\n<\/div>\n\n\n\n

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Thermal stress in Freiburg<\/h2>\n\n\n\n
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\"Karte
Number of hours in the Freiburg metropolitan area
with a UTCI (universal thermal climate index) >= 32\u00b0C per year as calculated by means of AI; mean of the results for the years 2019\u20132022. A UTCI >= 32\u00b0C corresponds to strong to extreme thermal stress (from
Briegel et al., 2024).<\/em>1<\/a><\/sup><\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Among other things, the researchers developed an AI-based model capable of depicting thermal stress on a small scale and over several decades. This artificial neural network (ANN) was applied to the entire city of Freiburg on the basis of regional climate data and calculated the thermal stress in high resolution down to the level of individual streets. The team calculated thermal stress with the help of three independent physical models and verified the results using data from a network of measuring stations located throughout the metropolitan area.<\/p>\n\n\n\n

\u2018It was very interesting for us to see that what the network predicted was so close to reality, although the network does not explicitly model the underlying physical processes. Only in this way could we observe such large prediction spaces\u2019, says computer scientist Prof. Dr. Thomas Brox<\/strong>, professor for pattern recognition and image processing at the University of Freiburg and head of the project.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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AI determines the position of trees<\/h2>\n\n\n\n

The model still needs to be merged with data on the vulnerability of individual urban spaces, but it can already be used now to evaluate urban planning measures such as the unsealing of surfaces. In addition, the researchers developed a novel method for automatically determining where to plant trees in a neighbourhood to achieve the maximum reduction of thermal stress. The method uses a classic machine learning algorithm to optimize the position of the trees.<\/p>\n\n\n\n

The researchers also developed an AI-based wind model and investigated, among other things, what impact local winds have on thermal stress in the urban environment. \u2018For climate research, the AI methods developed in the project open up entirely new possibilities for depicting the effects of climate change right at your doorstep\u2019, says Prof. Dr. Andreas Christen<\/strong>, professor for environmental meteorology at the University of Freiburg.<\/p>\n<\/div>\n\n\n\n

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\n\t\t\t\t\u201eIt was very interesting for us to see that what the network predicted was so close to reality, although the network does not explicitly model the underlying physical processes.\u201c\t\t\t<\/p>\n\t\t\t\t\t\t\t\t\t

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\n\t\t\t\tProf. Dr. Thomas Brox\t\t\t<\/h3>\n\t\t\t\t\t\t

\n\t\t\t\tProfessor for pattern recognition and image processing \t\t\t<\/h4>\n\t\t\t\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Potential for practice<\/h2>\n\n\n\n

In a simulation as well as on the basis of interviews with representatives from various departments of Freiburg\u2019s city administration, the researchers also studied where the project\u2019s AI-based tools could actually be put to good use in urban planning, what opportunities, risks, and challenges this would involve, and how the tools should be further adapted for practical use.<\/p>\n\n\n\n

\u2018I see the great potential of AI tools above all in the fact that they can simulate the effects of various planning scenarios more quickly, for example on heat development. This means that various interactions can be better assessed and incorporated into the planning process at an early stage\u2019, says Verena Hilgers<\/strong>, the City of Freiburg\u2019s climate adaptation manager.<\/p>\n\n\n\n

Usability and acceptance<\/h2>\n\n\n\n

Michael Bauder<\/strong>, head of the City of Freiburg\u2019s data science office, adds: \u2018The challenge is to integrate such an artificial neural network into the city\u2019s existing IT and data structures, to make it easy to use for professional users, and to continually develop the system further with large amounts of data\u2019.<\/p>\n\n\n\n

The project also addressed sociopolitical aspects, for example in two participatory ethics forums and other citizen participation activities. The first results of a survey among citizens in the Freiburg region already show differentiated attitudes towards AI applications in the public sector; opinions ranged from enthusiastic support to concerned scepticism.<\/p>\n<\/div>\n\n\n\n

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Many participating institutions<\/h2>\n\n\n\n

The project \u2018I4C \u2013 Intelligence for Cities\u2019 ran from the beginning of 2021 to the end of June 2024 and received around three million euros from the Federal Ministry for the Environment as an \u2018AI Lighthouse Project\u2019. The participating institutions were the University of Freiburg with numerous institutes and professorships, the two Freiburg Fraunhofer Institutes for Physical Measurements (IPM) and Solar Energy Systems (ISE), and the \u2018Energieagentur Regio Freiburg\u2019.<\/p>\n\n\n\n

The project was coordinated by the Sustainability Center Freiburg. \u2018This project brought together the broad spectrum of Freiburg\u2019s research competence on the topic of sustainability\u2019, says Christiane Felder<\/strong>, project coordinator and head of the central office of Sustainability Center Freiburg. \u2018Particularly exciting for me was the question of how the technological innovations researched in the project can contribute to solving current social challenges\u2019.<\/p>\n<\/div>\n<\/div>\n\n\t\t\t

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Geoscientific Model Development, 17, 1667\u20131688. <a href=\"#42c2d930-d735-4497-9a62-70583c594928-link\" aria-label=\"Jump to footnote reference 1\">\u21a9\ufe0e<\/a><\/li><\/ol>","protected":false},"excerpt":{"rendered":"<p>Freiburg, 05\/07\/2024 The Freiburg project \u2018I4C \u2013 Intelligence for Cities\u2019 has developed locally precise climate models and foundations for planning tools Thermal stress in Freiburg Among other things, the researchers developed an AI-based model capable of depicting thermal stress on a small scale and over several decades. This artificial neural network (ANN) was applied to<\/p>\n","protected":false},"author":73,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_trash_the_other_posts":false,"editor_notices":[],"_show_in_pageentry_slider":false,"_pageentry_slider_title":"","footnotes":"[{\"id\":\"42c2d930-d735-4497-9a62-70583c594928\",\"content\":\"Image source: Briegel F, Wehrle J, Schindler D, Christen A, 2024: High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning. Geoscientific Model Development, 17, 1667\\u20131688.\"}]"},"categories":[1],"tags":[],"class_list":["post-18531","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"featured_image_url":null,"featured_image_alt":"","_links":{"self":[{"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/posts\/18531","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/users\/73"}],"replies":[{"embeddable":true,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/comments?post=18531"}],"version-history":[{"count":5,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/posts\/18531\/revisions"}],"predecessor-version":[{"id":25179,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/posts\/18531\/revisions\/25179"}],"wp:attachment":[{"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/media?parent=18531"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/categories?post=18531"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uni-freiburg.de\/en\/wp-json\/wp\/v2\/tags?post=18531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}