Comparison Spatio-Temporal Prediction Approaches of Point-Referenced Environmental Data
| dc.contributor.author | Dorffer,Nina | |
| dc.contributor.author | Bruns,Julian | |
| dc.contributor.author | Abecker,Andreas | |
| dc.contributor.author | Lossow,Stefan | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:11Z | |
| dc.date.available | 2022-09-28T17:10:11Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Due to climate change and the effect on human health, there is an urgency to observe and understand the environment. To achieve this goal, knowledge about the development of environmental parameters over time and space is necessary. The analysis of the underlying data can therefore be done with spatial, temporal or even spatio-temporal methods. These methods can also be combined: First spatial,then temporal analyses and vice versa. In this work, we compare the effect of the decision whether to first analyze the data spatially, temporally or both simultaneously. We chose temperature data in Baden-Württemberg, yearly and monthly aggregated, for our comparison. | en |
| dc.identifier.doi | 10.18420/inf2022_129 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39503 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Environmental Computer Science | |
| dc.subject | Spatio-Temporal Statistics | |
| dc.subject | Evaluation | |
| dc.title | Comparison Spatio-Temporal Prediction Approaches of Point-Referenced Environmental Data | en |
| gi.citation.endPage | 1516 | |
| gi.citation.startPage | 1505 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Künstliche Intelligenz in der Umweltinformatik (KIU-2022) |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- kiu_06.pdf
- Größe:
- 1.76 MB
- Format:
- Adobe Portable Document Format