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Data Transfer and Extension for Mining Big Meteorological Data

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

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Abstract

It is necessary for mining meteorological big data to build a machine learning model by using historical data to predict the future meteorological elements. This work is significant and has a technical challenge. However, the maintained data of the small cities and the medium cities are very limited due to historical reasons. It is adverse to build an accurate forecast model. Aiming at this problem, a temperature forecast method based on transfer learning technique is proposed. It extends the data of the target city by transferring the data from related cities. It builds a forecast model based on the extended dataset, and then solves the problem of the insufficient samples in machine learning. In this experiment, the temperature sequence of Gaoyao weather station in Zhaoqing area is extended according to the yearly average temperature from 1884 to 1997 of Hongkong. It is corrected by Macau data. Temperature trend of Zhaoqing area is modeled by the time power function and the least square method. The fitting curves and the regression function of the temperature change are obtained. The forecasting model is tested by the actual temperature data of 2014, 2015 and 2016. The results support the effectiveness of the proposed method and they also justify the superiority of applying data transfer to temperature forecast.

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Acknowledgments

This research was supported by Science and technology research project of Guangdong Meteorological Bureau (Grant No.2016B51), Science and technology research project of Zhaoqing Meteorological Bureau (Grant No.201609), Science and technology innovation project of Zhaoqing (Grant No.201624030904).

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Authors and Affiliations

  1. Zhaoqing Meteorological Bureau, Zhaoqing, 526040, China

    Tianwen Huang

  2. Education Technology and Computer Center, Zhaoqing University, Zhaoqing, 526040, China

    Fei Jiao

Authors
  1. Tianwen Huang
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  2. Fei Jiao
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Corresponding author

Correspondence to Fei Jiao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Huang, T., Jiao, F. (2017). Data Transfer and Extension for Mining Big Meteorological Data. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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