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Precipitation Nowcasting using Residual Networks
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The aim of this paper is to investigate if rainfall prediction (nowcasting) can successively be made using a deep learning approach. The input to the networks are different spatiotemporal variables including forecasts from a NWP model. The results indicate that these networks has some predictive power and could be use in real application. Another interesting empirical finding relates to the usage of transfer learning from a domain which is not related instead of random initialization. Using pretrained parameters resulted in better convergence and overall performance than random initialization of the parameters.

Place, publisher, year, edition, pages
2018. , p. 45
Keywords [en]
Statistical learning, Neural networks, Deep learning, Convolutional neural networks, Residual network, Rainfall prediction
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-353154OAI: oai:DiVA.org:uu-353154DiVA, id: diva2:1216196
External cooperation
The Swedish Meteorological and Hydrological Institute (SMHI)
Subject / course
Statistics
Educational program
Master Programme in Statistics
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-11 Last updated: 2018-06-19Bibliographically approved

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Department of Statistics
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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