야외활동 의사결정을 위한 가중치 기반 기상정보 분석 알고리즘

Meteorological Information Analysis Algorithm based on Weight for Outdoor Activity Decision-Making

  • 이무훈 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 김민규 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Lee, Moo-Hun (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Kim, Min-Gyu (Weather Information Service Engine Institute, Hankuk University of Foreign Studies)
  • 투고 : 2015.12.08
  • 심사 : 2016.03.20
  • 발행 : 2016.03.28


최근 경제성장과 더불어 삶의 질이 향상됨에 따라 야외활동이 증가되었으며, 야외활동의 진행여부 의사결정은 기상여건과 밀접한 관계를 갖고 있다. 현재 이러한 야외활동 의사결정은 기상청의 일기예보와 주관적인 경험에 의해 결정되어지고 있다. 따라서, 야외활동 의사결정을 위해 기상정보를 기반으로 객관적 근거를 제시할 수 있는 분석 방법이 필요하다. 논문에서는 데이터마이닝을 기반으로 기상정보를 분석하여 야외활동 의사결정을 지원할 수 있는 기상정보 분석 알고리즘을 제안한다. 또한, 프로야구 일정 히스토리와 자동기상관측장비의 관측 자료를 데이터마이닝의 분류 알고리즘을 적용하여 실험을 수행하고, 제안한 알고리즘의 향상된 성능을 검증하였다.


기상정보;데이터마이닝;분류 알고리즘;의사결정지원 시스템;자동기상관측장비


연구 과제 주관 기관 : 기상청


  1. Seong-Hoon Lee, "Actual Cases and Analysis of IT Convergence for Green IT", Journal of the Korea Convergence Society, Vol. 6, No. 6, pp. 147-152, 2015.
  2. Young-Suk Chung, Rack-Koo Park, Jin-Mook Kim, "Study on predictive modeling of incidence of traffic accidents caused by weather conditions", Journal of the Korea Convergence Society, Vol. 5, No. 1, pp. 9-15, 2014.
  3. Byeongyong Hyeon, Yonghee Lee, Kisung Seo, "Evolutionary Nonlinear Regression Based Compensation Technique for Short-range Prediction of Wind Speed using Automatic Weather Station", The Transactions of the Korean Institute of Electrical Engineers, Vol. 64, No. 1, pp. 107-112, 2015.
  4. Seul-Gi Lee, Sung-Gwan Jung, Woo-sung Lee, Kyung-hun Park, "A Predictive Model for Urban Temperature using the Artificial Neural Network", Journal of the Korea Planning Association, Vol. 46, No. 1, pp. 129-142, 2011.
  5. Boosik Kang, Bongki Lee, "Prediction Probability of Precipitation Using Artificial Neural Network and Mesoscale Numerical Weather Prediction", Journal of the Korean Society of Civil Engineers, Vol. 28, No. 5B, pp. 485-493, 2008.
  6. T. Hall, H. E. Brooks, C. A. Doswell, "Rrecipitation forcasting using a neural network", Weather and Forecasting, Vol. 14, No. 3, pp. 338-345, 1999.<0338:PFUANN>2.0.CO;2
  7. K. C. Luk, J. E. Ball, A. Sharma, "An application of artificial neural networks for rainfall forecasting", Mathematical and Computer Modelling, Vol. 33, No. 6, pp. 638-693, 2001.
  8. J. N. K. Liu and R. S. T. Lee, "Rainfall forecasting from multiple point sources using neural networks", In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Vol. 3, pp. 429-434, 1999.
  9. Michael Mayo, "Random convolution ensembles", Advances in Multimedia Information Processing - PCM 2007, 8th Pacific Rim Conference on Multimedia, Lecture Notes in Computer Science 4810, pp. 216-225. Springer, 2007.
  10. RJ Durrant, A Kaban, "Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions", Machine Learning, Vol. 99, No. 2, pp. 257-286, 2014.
  11. Sally Jo Cunningham, Eibe Frank, "Market basket analysis of library circulation data", Proc 6th International Conference on Neural Information Processing, Vol. 2, pp. 825-830, 1999.
  12. Ian H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, and Sally Jo Cunningham, "Weka: Practical machine learning tools and techniques with Java implementations", Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems, pp. 192-196, 1999.
  13. Ross Quinlan, "C4.5 Programs for Machine Learning", Morgan Kaufmann Publishers, San Mateo, CA, 1993.
  14. Jan N van Rijn, Geoffrey Holmes, Bernhard Pahringer, and Joaquin Vanschoren, "Algorithm selection on data streams", Proc 17th International Conference on Discovery Science, pp. 325-336, Springer, 2014.
  15. William W. Cohen, "Fast Effective Rule Induction", International Conference on Machine Learning, pp. 115-123. 1995.
  16. Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, and Geoff Holmes, "Active learning with drifting streaming data", IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 1, pp. 27-39, 2014.
  17. N. Bhatia, "Survey of Nearest Neighbor Techniques", International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010.
  18. D. Aha, D. Kibler, "Instance-based learning algorithms". Machine Learning. pp. 37-66, 1991.
  19. Deza, M.; Deza, E. "Encyclopedia of Distances", Springer-Verlag, pp.94, 2009.
  20. David M. J. Tax, Robert Duin, and Dick De Ridder, "Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB", John Wiley and Sons. pp. 440, 2004.
  21. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R. K. Murthy, "Improvements to Platt's SMO Algorithm for SVM Classifier Design", Neural Computation. Vol. 13, No. 3, pp. 637-649. 2001.
  22. George H. John, Pat Langley, "Estimating Continuous Distributions in Bayesian Classifiers", Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338-345, 1995.