Journal of the Korean Institute of Gas (한국가스학회지)

The Korean Institute of GAS (한국가스학회)
권호 표지
DOI QR코드

DOI QR Code

The Study of Prediction Model of Gas Accidents Using Time Series Analysis

시계열 분석을 이용한 가스사고 발생 예측 연구

  • Lee, Su-Kyung (Graduate School of Energy & Environment, Seoul National Univ. of Technology) ;
  • Hur, Young-Taeg (Korea Gas Safety Corporatio) ;
  • Shin, Dong-Il (Dept. of chemical Engineering, Myongji University) ;
  • Song, Dong-Woo (Graduate School of Energy & Environment, Seoul National Univ. of Technology) ;
  • Kim, Ki-Sung (Graduate School of Energy & Environment, Seoul National Univ. of Technology)
  • 이수경 (서울과학기술대학교 에너지환경대학원) ;
  • 허영택 (한국가스안전공사) ;
  • 신동일 (명지대학교) ;
  • 송동우 (서울과학기술대학교 에너지환경대학원) ;
  • 김기성 (서울과학기술대학교 에너지환경대학원)
  • Received : 2013.11.26
  • Accepted : 2014.02.11
  • Published : 2014.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.

본 연구에서는 국내에서 발생한 가스사고를 분석하여 가스사고의 건수예측모델에 대하여 제시하였다. 가스사고 건수를 예측하기 위하여 단순이동평균법(3,4,5기간), 가중이동평균법 및 지수평활법을 적용해 본 결과, 4기간 이동평균법과 가중이동평균법에 의한 모델의 평균오차제곱합이 44.4와 43으로 가장 정확성이 높은 것으로 나타났다. 가스사고 발생건수 예측시스템을 개발함으로서 가스사고 예방활동에 적극 활용할 수 있을 것이다.

Keywords

References

  1. Heo, In-Hye and Lee, Seung-Ho., The Impact of Climate Change on Ski Industries in South Korea, Journal of the Korean Geographical Society, Vol. 43(5), 715-727, (2008)
  2. Wu Wen-feng, Wu Wan-qing, Feng Xing,Wang Zhen-mao, "Study on the Frequency Prediction of Marine Accidents for Ship on the Sea", Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on, 1-4, (2011)
  3. Lee, Dong-Min, Kim, Do-Hoon, Sung, Nakmoon, "Development of a Accident Frequency Prediction Model at Rural Multi-Lane Highways", KOR-KST, 27(4), 207-215, (2009)
  4. Heo, Young- Taeg, Shin, Dong-Il, Lee, Su- Kyung, "Data Mining of Gas Accident and Meteorological Data in korea for a prediction Model of Gas Accidents", KIGAS, 16(1), 33-38, (2012) https://doi.org/10.7842/kigas.2012.16.1.33
  5. Harvey, Andrew C. "Time Series Models", Cambridge, MA, MIT Press, (1993)
  6. Zou, H., Yang, Y. H., "Combining Time Series Model for Forecasting", International Journal of Forecasting, 20(1), 69-84, (2004) https://doi.org/10.1016/S0169-2070(03)00004-9
  7. Godfrey, L. "Testing the Adequancy of a Time Series Model", Biometrika, 66(0), 67-72, (1979) https://doi.org/10.1093/biomet/66.1.67
  8. Lim, C., McAleer, Michael, "Time Series Forecasts of International Travel Demand for Australia", Tourism Management, 23(4), 389-396, (2002) https://doi.org/10.1016/S0261-5177(01)00098-X
  9. Box, George E. P., Gwilym M. J., " Time Series Analysis : Forecasting and Control", Revised Edition, Oakland, CA., Holden-Day, (1976)