Neural network and regression methods for optimizations between two meteorological factors

Ki Hong Shin, Woonhak Baek, Kyungsik Kim, Cheol Hwan You, Ki Ho Chang, Dong In Lee, Seong Soo Yum

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper is concerned with the temporal variation characteristics of two meteorological factors (temperature and humidity) in four metropolitan cities (Seoul, Busan, Daegu, Daejeon) in South Korea. Data are extracted from seven years (2008 to 2014) of hourly time series data in meteorological offices of the Korea Meteorological Administration. Using the detrended cross-correlation analysis (DCCA) method, the DCCA coefficient of temperature is compared to that of humidity from daily time series data during four seasons in the four metropolitan cities. In particular, as window size s increases, the DCCA cross-correlation coefficient approaches 0.034 at a time lag of 14 days in the case of spring in Seoul. We find the weak cross-correlation between temperature and humidity at different time lags of 14, 21 and 28 days in spring in Seoul, and the errors ET in the ANN are relatively larger values than that of any other season in both the ANN and the MRM. Particularly, in the ANN model, there exist relatively a large error value of temperature as the characteristics of the non-stationary, deterministically chaotic and noisy meteorological data extracted for the short-term prediction rather than the long-term prediction.

Original languageEnglish
Pages (from-to)778-796
Number of pages19
JournalPhysica A: Statistical Mechanics and its Applications
Publication statusPublished - 2019 Jun 1

Bibliographical note

Funding Information:
This work was funded by the Korea Meteorological Administration Research and Development Program “Research and Development for KMA Weather, Climate, and Earth system Services-Support to Use of Meteorological Information and Value Creation” under Grant ( KMA2018-00222 ) and by the Korea Meteorological Institute under Grant KMI 2018-05410 .

Publisher Copyright:
© 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics


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