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dc.contributor.authorGygax, Gregory-
dc.contributor.authorSchüle, Martin-
dc.date.accessioned2020-11-25T10:26:30Z-
dc.date.available2020-11-25T10:26:30Z-
dc.date.issued2020-09-02-
dc.identifier.isbn978-3-030-58308-8de_CH
dc.identifier.isbn978-3-030-58309-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20891-
dc.description.abstractForecasting the weather is a great scientific challenge. Physics-based, numerical weather prediction (NWP) models have been developed for decades by large research teams and the accuracy of forecasts has been steadily increased. Yet, recently, more and more data-driven machine learning approaches to weather forecasting are being developed. In this contribution we aim to develop an approach that combines the advantages of both methodologies, that is, we develop a deep learning model to predict air temperature that is trained both on NWP models and local weather data. We evaluate the approach for 249 weather station sites in Switzerland and find that the model outperfoms the NWP models on short time-scales and in some geographically distinct regions of Switzerland.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMachine learningde_CH
dc.subjectDeep learningde_CH
dc.subjectWeather predictionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc551: Geologie und Hydrologiede_CH
dc.titleA hybrid deep learning approach for forecasting air temperaturede_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-58309-5_19de_CH
zhaw.conference.details9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end246de_CH
zhaw.pages.start235de_CH
zhaw.parentwork.editorSchilling, Frank-Peter-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number12294de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedBio-Inspired Modelling and Learning Systemsde_CH
zhaw.funding.zhawAn integrated modelling and learning framework for real-time online decision assistance in Swiss agriculturede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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