Deep neural network based feature representation for weather forecasting

James N.K. Liu, Yanxing Hu, Jane Jia You, Pak Wai Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

32 Citations (Scopus)

Abstract

This paper concentrated on a new application of Deep Neural Network (DNN) approach. The DNN, also widely known as Deep Learning(DL), has been the most popular topic in research community recently. Through the DNN, the original data set can be represented in a new feature space with machine learning algorithms, and intelligence models may have the chance to obtain a better performance in the “learned” feature space. Scientists have achieved encouraging results by employing DNN in some research fields, including Computer Vision, Speech Recognition, Natural Linguistic Programming and Bioinformation Processing. However, as an approach mainly functioned for learning features, DNN is reasonably believed to be a more universal approach: it may have the potential in other data domains and provide better feature spaces for other type of problems. In this paper, we present some initial investigations on applying DNN to deal with the time series problem in meteorology field. In our research, we apply DNN to process the massive weather data involving millions of atmosphere records provided by The Hong Kong Observatory (HKO)1. The obtained features are employed to predict the weather change in the next 24 hours. The results show that the DNN is able to provide a better feature space for weather data sets, and DNN is also a potential tool for the feature fusion of time series problems.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014
EditorsHamid R. Arabnia, David de la Fuente, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti
PublisherCSREA Press
Pages105-110
Number of pages6
ISBN (Electronic)1601322763, 9781601322760
Publication statusPublished - Jul 2014
Event2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014 - Las Vegas, United States
Duration: 21 Jul 201424 Jul 2014

Publication series

NameProceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014

Conference

Conference2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014
Country/TerritoryUnited States
CityLas Vegas
Period21/07/1424/07/14

Keywords

  • Deep neural network
  • Feature representation
  • Stacked auto-encoder
  • Weather forecasting

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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