Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes
| dc.contributor.author | Tremper, Paul | |
| dc.contributor.author | Till Riedel | |
| dc.contributor.author | Budde, Matthias | |
| dc.date.accessioned | 2021-12-14T10:57:18Z | |
| dc.date.available | 2021-12-14T10:57:18Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.18420/informatik2021-022 | |
| dc.identifier.isbn | 978-3-88579-708-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37685 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2021 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-314 | |
| dc.subject | Air Quality | |
| dc.subject | Gaussian Process Regression | |
| dc.subject | Validation | |
| dc.title | Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes | en |
| gi.citation.endPage | 286 | |
| gi.citation.startPage | 269 | |
| gi.conference.date | 27. September - 1. Oktober 2021 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | 2. Workshop Künstliche Intelligenz in der Umweltinformatik (KIUI-2021) |
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