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Weighing Machine Learning Algorithms for Accounting RWISs Characteristics in METRo: A comparison of Random Forest, Deep Learning & kNN
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), laid the foundation of solving the energy balance and calculating the temperature evolution of roads. METRo does this by providing a numerical modelling system making use of Road Weather Information Stations (RWIS) and meteorological projections. While METRo accommodates tools for correcting errors at each station, such as regional differences or microclimates, this thesis proposes machine learning as a supplement to the METRo prognostications for accounting station characteristics. Controlled experiments were conducted by comparing four regression algorithms, that is, recurrent and dense neural network, random forest and k-nearest neighbour, to predict the squared deviation of METRo forecasted road surface temperatures. The results presented reveal that the models utilising the random forest algorithm yielded the most reliable predictions of METRo deviations. However, the study also presents the promise of neural networks and the ability and possible advantage of seasonal adjustments that the networks could offer.

Place, publisher, year, edition, pages
2019. , p. 73
Keywords [en]
machine learning, neural network, random forest, k-nearest neighbour, model of the environment and temperature of roads
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-85586OAI: oai:DiVA.org:lnu-85586DiVA, id: diva2:1326944
External cooperation
Klimator AB
Subject / course
Computer Science
Educational program
Datavetenskap, kandidatprogram, 60 hp
Supervisors
Examiners
Available from: 2019-06-19 Created: 2019-06-18 Last updated: 2019-06-19Bibliographically approved

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fulltext(1451 kB)31 downloads
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Landmér Pedersen, Jesper
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CiteExportLink to record
Permanent link

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Cite
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
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  • Other locale
More languages
Output format
  • html
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