Neural network and regression methods for optimizations between two meteorological factors.

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 E T 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. • The temporal variation characteristics of two meteorological factors are studied in four metropolitan cities. • This presents the status of simulations and analyses relevant to the DCCA, ANN, and MRM. • The DCCA coefficients for temperature and humidity in all seasons are estimated and compared in four metropolitan cities. • The error EH for humidity had a smaller value compared to the error ET for temperature. • We find that the MRM outperforms the ANN for two meteorological factors. [ABSTRACT FROM AUTHOR]