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Abstract

Urban climate patterns affect the quality of life of growing urban populations. Studying microclimate patterns, particularly relating to heat, is key to protecting urban residents. The morphology of urban neighborhoods affects local weather patterns, and the development of new neighborhoods could potentially affect future weather. Given the complexity of these relationships, machine learning is a perfect candidate for analyzing the data. This study leverages an adversarial network, containing two competing models, to predict future neighborhood possibilities given the land cover in the area. The model has been trained on data from Los Angeles, California, with the images divided into residential, commercial, and mixed neighborhoods. These divisions allow for patterns and predictions to be analyzed on a neighborhood-specific level, addressing the effects of building distribution on localized weather patterns. Once these predictions have been made, they can be fed into existing models and the impact on climate can be examined.

This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Acknowledgements

This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship program.

This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Support for DOI 10.13139/ORNLNCCS/1774134 dataset is provided by the U.S. Department of Energy, project Automatic Building Energy Modeling (AutoBEM) under Contract DE-AC05-00OR22725. Project Automatic Building Energy Modeling (AutoBEM) used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725

This study was completed under the sponsorship of the DOE Office of Science as a part of the research in Multi-Sector Dynamics within the Earth and Environmental System Modeling Program as part of the Integrated Multiscale Multisector Modeling (IM3) Scientific Focus Area led by Pacific Northwest National Laboratory.

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Correspondence to Abigail R. Wheelis .

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Wheelis, A.R., Sweet-Breu, L.T., Allen-Dumas, M.R. (2022). Patterns and Predictions: Generative Adversarial Networks for Neighborhood Generation. In: Doug, K., Al, G., Pophale, S., Liu, H., Parete-Koon, S. (eds) Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation. SMC 2022. Communications in Computer and Information Science, vol 1690. Springer, Cham. https://doi.org/10.1007/978-3-031-23606-8_24

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