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dc.contributor.authorSchmitz, Christian
dc.contributor.authorSerai, Dhiren Devinder
dc.contributor.authorEscobar Gava, Tatiane
dc.contributor.editorMeyer, Holger
dc.contributor.editorRitter, Norbert
dc.contributor.editorThor, Andreas
dc.contributor.editorNicklas, Daniela
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.date.accessioned2019-04-15T11:40:38Z
dc.date.available2019-04-15T11:40:38Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-684-8
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21822
dc.description.abstractCities worldwide are facing air quality issues, leading to bans of vehicles and lower quality of life for inhabitants. We forecast the air quality for Stuttgart based on expected weather condition. For that purpose, we extract, cleanse, and integrate the DHT22 and SDS11 sensors’ data to feed two different machine learning models for predicting the particulate matter values for the near future.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019 – Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-290
dc.subjectData Science Challenge
dc.subjectBig Data Analytics
dc.titlePrediction of air pollution with machine learningen
mci.reference.pages303-304
mci.conference.sessiontitleData Science Challenge 2019
mci.conference.locationRostock
mci.conference.date4.-8. März 2019
dc.identifier.doi10.18420/btw2019-ws-34


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