A hybrid deep learning approach for forecasting air temperature

Gygax, Gregory; Schüle, Martin

doi:10.1007/978-3-030-58309-5_19

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	A hybrid deep learning approach for forecasting air temperature
Authors:	Gygax, Gregory Schüle, Martin
et. al:	No
DOI:	10.1007/978-3-030-58309-5_19
Proceedings:	Artificial Neural Networks in Pattern Recognition
Editors of the parent work:	Schilling, Frank-Peter Stadelmann, Thilo
Page(s):	235
Pages to:	246
Conference details:	9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020
Issue Date:	2-Sep-2020
Series:	Lecture Notes in Computer Science
Series volume:	12294
Publisher / Ed. Institution:	Springer
Publisher / Ed. Institution:	Cham
ISBN:	978-3-030-58308-8 978-3-030-58309-5
Language:	English
Subjects:	Machine learning; Deep learning; Weather prediction
Subject (DDC):	006: Special computer methods 551: Geology and hydrology
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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/20891
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project:	An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture
Appears in collections:	Publikationen Life Sciences und Facility Management

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Gygax, G., & Schüle, M. (2020). A hybrid deep learning approach for forecasting air temperature [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 235–246). Springer. https://doi.org/10.1007/978-3-030-58309-5_19

Gygax, G. and Schüle, M. (2020) ‘A hybrid deep learning approach for forecasting air temperature’, in F.-P. Schilling and T. Stadelmann (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 235–246. Available at: https://doi.org/10.1007/978-3-030-58309-5_19.

G. Gygax and M. Schüle, “A hybrid deep learning approach for forecasting air temperature,” in Artificial Neural Networks in Pattern Recognition, Sep. 2020, pp. 235–246. doi: 10.1007/978-3-030-58309-5_19.

GYGAX, Gregory und Martin SCHÜLE, 2020. A hybrid deep learning approach for forecasting air temperature. In: Frank-Peter SCHILLING und Thilo STADELMANN (Hrsg.), Artificial Neural Networks in Pattern Recognition. Conference paper. Cham: Springer. 2 September 2020. S. 235–246. ISBN 978-3-030-58308-8

Gygax, Gregory, and Martin Schüle. 2020. “A Hybrid Deep Learning Approach for Forecasting Air Temperature.” Conference paper. In Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, 235–46. Cham: Springer. https://doi.org/10.1007/978-3-030-58309-5_19.

Gygax, Gregory, and Martin Schüle. “A Hybrid Deep Learning Approach for Forecasting Air Temperature.” Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, Springer, 2020, pp. 235–46, https://doi.org/10.1007/978-3-030-58309-5_19.