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Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes

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2021

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Gesellschaft für Informatik, Bonn

Zusammenfassung

The central question of this paper is whether interpolation techniques applied to a distributed sensor network can indeed provide more information than using the constant background of an urban reference station to measure air pollution. We compare different interpolation techniques based on temporal-spatial machine learning in terms of their applicability for correctly predicting personal exposure. Using a dataset of stationary low-cost sensors, we estimate exposure on a route through the city and compare it to mobile measurements. The results show that while different machine learning-based interpolation methods yield quite different results, validation of machine learning-based approaches is still challenging.

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Tremper, Paul; Till Riedel; Budde, Matthias (2021): Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes. INFORMATIK 2021. DOI: 10.18420/informatik2021-022. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-708-1. pp. 269-286. 2. Workshop Künstliche Intelligenz in der Umweltinformatik (KIUI-2021). Berlin. 27. September - 1. Oktober 2021

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