Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gygax, Gregory | - |
dc.contributor.author | Schüle, Martin | - |
dc.date.accessioned | 2020-11-25T10:26:30Z | - |
dc.date.available | 2020-11-25T10:26:30Z | - |
dc.date.issued | 2020-09-02 | - |
dc.identifier.isbn | 978-3-030-58308-8 | de_CH |
dc.identifier.isbn | 978-3-030-58309-5 | de_CH |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/20891 | - |
dc.description.abstract | Forecasting 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.iso | en | de_CH |
dc.publisher | Springer | de_CH |
dc.relation.ispartofseries | Lecture Notes in Computer Science | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | Machine learning | de_CH |
dc.subject | Deep learning | de_CH |
dc.subject | Weather prediction | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.subject.ddc | 551: Geologie und Hydrologie | de_CH |
dc.title | A hybrid deep learning approach for forecasting air temperature | de_CH |
dc.type | Konferenz: Paper | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | Life Sciences und Facility Management | de_CH |
zhaw.organisationalunit | Institut für Computational Life Sciences (ICLS) | de_CH |
zhaw.publisher.place | Cham | de_CH |
dc.identifier.doi | 10.1007/978-3-030-58309-5_19 | de_CH |
zhaw.conference.details | 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.pages.end | 246 | de_CH |
zhaw.pages.start | 235 | de_CH |
zhaw.parentwork.editor | Schilling, Frank-Peter | - |
zhaw.parentwork.editor | Stadelmann, Thilo | - |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.series.number | 12294 | de_CH |
zhaw.publication.review | Peer review (Publikation) | de_CH |
zhaw.title.proceedings | Artificial Neural Networks in Pattern Recognition | de_CH |
zhaw.webfeed | Bio-Inspired Modelling and Learning Systems | de_CH |
zhaw.funding.zhaw | An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture | de_CH |
zhaw.author.additional | No | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen Life Sciences und Facility Management |
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