Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

The Impact of Severe Weather Announcement on the Korea Meteorological Administration Call Center Counseling Demand

기상 특보 발표가 기상청 콜센터 상담 건수에 미치는 영향 분석

  • Ji, Youngmi (Department of Applied Statistics, Yonsei University) ;
  • Park, Taeyoung (Department of Applied Statistics, Yonsei University) ;
  • Lee, Yung-Seop (Department of Statistics, Dongguk University)
  • 지영미 (연세대학교 응용통계학과) ;
  • 박태영 (연세대학교 응용통계학과) ;
  • 이영섭 (동국대학교 통계학과)
  • Received : 2017.07.03
  • Accepted : 2017.10.12
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The effective management of call centers under special circumstances is critical to improve customer satisfaction. In order to effectively respond to call center counseling demand, this paper aims to identify factors having the greatest impact on the number of Korea Meteorological Administration (KMA) call center counseling. To do so, we propose to combine call center data with severe weather announcement data and investigate how the severe weather announcement affects the number of KMA call center counseling. A time lag analysis is conducted and it is found that the severe weather announcement takes about an hour to be reflected in the number of KMA call center counseling. Based on the result of the time lag analysis, we conduct a comparative analysis according to time and season using the data collected from 1 January 2012, to 29 June 2016. The results show that the number of KMA call center counseling increases at lunchtime and decreases during nighttime, and the average rate of change in call center counseling demand tends to be larger under the severe weather announcement. For the comparative analysis according to the season, there are significant differences in the effect of severe weather announcement on the number of KMA call center counseling in spring, fall and winter.

Keywords

References

  1. Baek, W., N. G. Kim, and H. C. Kim, 2009: A case study on predicting inbound calls of motor insurance company using interactive decision tree analysis. Proc. of the KIIS Autumn Conf. 2009, 203-210 (in Korean).
  2. Baek, W., N. G. Kim, and H. C. Kim, 2010: A case study on forecasting inbound calls of motor insurance company using interactive data mining technique. J. Intell. Inf. Syst., 16, 99-120 (in Korean with English abstract).
  3. Hyeon, B., Y.-H. Lee, and K. Seo, 2014: A prediction algorithm for a heavy rain newsflash using the evolutionary symbolic regression technique. J. Inst. Control. Robot. Syst., 20, 730-735, doi:10.5302/J.ICROS.2014.13.1984 (in Korean with English abstract).
  4. Kim, S.-M., J.-E. Nah, and S.-M. Kim, 2011: The staffing problem at the call center by optimization and simulation. IE Interfaces, 24, 40-50, doi:10.7232/IEIF.2011. 24.1.040 (in Korean with English abstract).
  5. Kim, Y.-B., and S.-M. Kim, 2014: Marine meteorological characteristics by comparison of high wind-wave alert and moored buoy data off the coast of the East Sea between 2006 and 2013. J. Fish. Mar. Sci. Edu., 26, 1013-1025, doi:10.13000/JFMSE.2014.26.5.1013(in Korean with English abstract).
  6. Lee, Y.-S., C. Lim, M. Heo, and H. Kim, 2016: Analysis of data from the weather call center using a text-mining technique. Proc. of the Spring Meeting of KMS, 153-154 (in Korean).
  7. Park, H. J., H. Kim, T. Park, and Y.-S. Lee, 2016: Analysis of patterns in meteorological research and development using a text-mining algorithm. Korean J. Appl. Stat., 29, 935-947 (in Korean with English abstract). https://doi.org/10.5351/KJAS.2016.29.5.935
  8. Seo, J.-H., Y.-H. Lee, and Y.-H. Kim, 2012: Short-term drought prediction based on machine learning. Proc. of KIIS Fall Conf. 2012, 22, 165-167 (in Korean).
  9. Song, Y., C. Lim, J. Joo, and M. Park, 2016: A study on heavy rain forecast evaluation and improvement method. J. Korean Soc. Hazard Mitig., 16, 113-121, doi:10.9798/KOSHAM.2016.16.2.113 (in Korean with English abstract).