한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제51권5호
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  • Pages.383-393
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  • 2018
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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Self-organizing map을 이용한 강우 지역빈도해석의 지역구분 및 적용성 검토

Assessing applicability of self-organizing map for regional rainfall frequency analysis in South Korea

  • 안현준 (연세대학교 토목환경공학과) ;
  • 신주영 (연세대학교 토목환경공학과) ;
  • 정창삼 (인덕대학교 토목공학과) ;
  • 허준행 (연세대학교 토목환경공학과)
  • Ahn, Hyunjun (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Shin, Ju-Young (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Jeong, Changsam (Department of Civil Engineering, Induk University) ;
  • Heo, Jun-Haeng (Department of Civil and Environmental Engineering, Yonsei University)
  • 투고 : 2018.01.15
  • 심사 : 2018.02.02
  • 발행 : 2018.05.31
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초록

지역빈도해석은 대상 지점과 수문학적 동질성을 만족하는 주변 지점을 하나의 지역으로 보고 빈도해석을 수행하는 방법이다. 따라서 동질한 지역의 구분은 지역빈도해석에 있어서 가장 중요한 가정이라고 할 수 있다. 이에 본 연구에서는 인공신경망 기법중 하나인 자기조직화지도(self-organizing map, SOM) 기법을 활용하여 강우 지역빈도해석을 위한 동질 강수 지역을 구분하였다. 지역구분 인자로는 지형 정보와 시 단위 강우 자료를 활용하였다. 최적 SOM 지도 구성을 위해 정량적 오차와 위상관계 오차를 활용하였다. 그 결과 $7{\times}6$ 배열의 42개의 노드를 갖는 모형을 선정하였고 최종적으로 강우 지역빈도해석을 위해 6개의 군집으로 구분하였다. 동질성 검토 결과 6개의 군집 모두 동질한 지역으로 나타났으며 기존의 유사하게 구분된 지역들과 이질성 척도를 비교하였을 때 좀 더 안정적인 지역 구분결과를 나타내는 것을 확인하였다.

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.

키워드

참고문헌

  1. Ahn, S.-H., Kim, B.-J., Lee, S.-L., and Kim, H.-K. (2008). "The characteristics of disaster by track of typhoon affecting the Korean peninsula." Journal of The Korean Society of Hazard Mitigation, Vol. 8, No. 3, pp. 29-36.
  2. Arribas-Bel, D., Nijkamp, P., and Scholten, H. (2011). "Multi- dimensional urban sprawl in Europe: a self-organizing map approach." Computers, Environment and Urban Systems, Vol. 35, No. 4, pp. 263-275. https://doi.org/10.1016/j.compenvurbsys.2010.10.002
  3. Bärring, L. (1988). "Regionalization of daily rainfall in Kenya by means of common factor analysis." International Journal of Climatology, Vol. 8, No. 4, pp. 371-389. https://doi.org/10.1002/joc.3370080405
  4. Burn, D. H. (1989). "Cluster analysis as applied to regional flood frequency." Journal of Water Resources Planning and Management, Vol. 115, No. 5, pp. 567-582. https://doi.org/10.1061/(ASCE)0733-9496(1989)115:5(567)
  5. Cunnane, C. (1989). Statistical distributions for flood frequency analysis. Operational Hydrology Report, No. 33.
  6. Darlymple, T. (1960). "Flood frequency analyses." Water Supply Paper, U.S. Geological Survey, Reston, VA., p. 80.
  7. Dinpashoh, Y., Fakheri-Fard, A., Moghaddam, M., Jahanbakhsh, S., and Mirnia, M. (2004). "Selection of variables for the purpose of regionalization of Iran's precipitation climate using multivariate methods." Journal of Hydrology, Vol. 297, No. 1, pp. 109-123. https://doi.org/10.1016/j.jhydrol.2004.04.009
  8. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). "From data mining to knowledge discovery in databases." AI Magazine, Vol. 17, No. 3, p. 37.
  9. Greis, N. P., and Wood, E. F. (1981). "Regional flood frequency estimation and network design." Water Resources Research, Vol. 17, No. 4, pp. 1167-1177. https://doi.org/10.1029/WR017i004p01167
  10. Guttman, N. B. (1993). "The use of L-moments in the determination of regional precipitation climates." Journal of Climate, Vol. 6, No. 12, pp. 2309-2325. https://doi.org/10.1175/1520-0442(1993)006<2309:TUOLMI>2.0.CO;2
  11. Heo, J. H. (2016). Statistical hydrology. Gumi Press, Seoul, Korea.
  12. Heo, J. H., Boes, D. C., and Salas, J. D. (1990). Regional flood frequency modeling and estimation. Water Resources Papers, No.101, Colorado State University, Fort Collins, CO.
  13. Heo, J.-H., Lee, Y. S., Shin, H., and Kim, K.-D. (2007). "Application of regional rainfall frequency analysis in South Korea (I): rainfall quantile estimation." Journal of the Korean Society of Civil Engineers B, Vol. 27, No. 2B, pp. 101-111.
  14. Hewitson, B., and Crane, R. (2002). "Self-organizing maps: applications to synoptic climatology." Climate Research, Vol. 22, No. 1, pp. 13-26. https://doi.org/10.3354/cr022013
  15. Hosking, J., and Wallis, J., (1997). Regional frequency analysis: an approach based on L-moments. Cambridge University Press, NY.
  16. Hosking, J., Wallis, J. R., and Wood, E. F. (1985). "An appraisal of the regional flood frequency procedure in the UK flood studies report." Hydrological Sciences Journal, Vol. 30, No. 1, pp. 85-109. https://doi.org/10.1080/02626668509490973
  17. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). "Data clustering: a review." ACM Computing Surveys (CSUR), Vol. 31, No. 3, pp. 264-323. https://doi.org/10.1145/331499.331504
  18. Jeong, K.-S., Hong, D.-G., Byeon, M.-S., Jeong, J.-C., Kim, H.-G., Kim, D.-K., and Joo, G.-J. (2010). "Stream modification patterns in a river basin: Field survey and self-organizing map (SOM) application." Ecological Informatics, Vol. 5, No. 4, pp. 293-303. https://doi.org/10.1016/j.ecoinf.2010.04.005
  19. Jeong, K.-S., Kim, D.-K., Shin, H.-S., Yoon, J.-D., Kim, H.-W., and Joo, G.-J. (2011). "Impact of summer rainfall on the seasonal water quality variation (chlorophyll a) in the regulated Nakdong river." KSCE Journal of Civil Engineering, Vol. 15, No. 6, pp. 983-994. https://doi.org/10.1007/s12205-011-1052-9
  20. Jun, S., and Choi, Y. (2013). "The applicability of the self-organizing map method for synoptic climatology studies over the Republic of Korea." Journal of Climate Research, Vol. 8, No. 1, pp. 1-11.
  21. Kiang, M. Y., Kulkarni, U. R., Goul, M., Philippakis, A., Chi, R., and Turban, E. (1997). "Improving the effectiveness of self- organizing map networks using a circular Kohonen layer, System Sciences, 1997." Proceedings of the Thirtieth Hawaii International Conference on. IEEE, pp. 521-529.
  22. Kiang, M., Kulkarni, U., and St Louis, R. (2001). "Circular/wrap-around self-organizing map networks: an empirical study in clustering and classification." Journal of the Operational Research Society, Vol. 52, No. 1, pp. 93-101. https://doi.org/10.1057/palgrave.jors.2601049
  23. Kim, H.-U., Shim, J.-K., and Choi, B.-C. (2017). "Analysis on rainfall pattern during typhoons using self-organizing map." Korean Society of Hazard Mitigation, Vol. 17, No. 4, pp. 55-64.
  24. Kohonen, O., and Hauta-Kasari, M. (2008). "Distance measures in the training phase of self-organizing map for color histogram generation in spectral image retrieval." Journal of Imaging Science and Technology, Vol. 52, No. 2, pp. 20201-1 - 20201-11.
  25. Kohonen, T. (1998). "The self-organizing map." Neurocomputing, Vol. 21, No. 1, pp. 1-6. https://doi.org/10.1016/S0925-2312(98)00030-7
  26. Kohonen, T. (2001). Self-organizing maps. 3rd Edition, Springer, Berlin.
  27. Kohonen, T. (2007). "The self-organizing map." Proceedings of the IEEE, Vol. 9, pp. 1464-1479.
  28. Lee, S. W., and Choi, G. Y. (2013). "Spatio-temporal patterns of extreme precipitation events by typhoons across the Republic of Korea." Journal of The Korean Association of Regional Geographers, Vol. 19, No. 3, pp. 384-400.
  29. Lee, Y. S., Heo, J.-H., Nam, W. S., and Kim, K.-D. (2007). "Application of regional rainfall frequency analysis in South Korea (II): Monte Carlo simulation and determination of appropriate method." Journal of the Korean Society of Civil Engineers B, Vol. 27, No. 2B, pp. 113-123.
  30. Lettenmaier, D. P., and Potter, K. W. (1985). "Testing flood frequency estimation methods using a regional flood generation model." Water Resources Research, Vol. 21, No. 12, pp. 1903-1914. https://doi.org/10.1029/WR021i012p01903
  31. Liu, Y., Weisberg, R. H., and Mooers, C. N. (2006). "Performance evaluation of the self-organizing map for feature extraction." Journal of Geophysical Research: Oceans, Vol. 111, No. C5.
  32. Lu, H.-C., Chang, C.-L., and Hsieh, J.-C. (2006). "Classification of PM 10 distributions in Taiwan." Atmospheric Environment, Vol. 40, No. 8, pp. 1452-1463. https://doi.org/10.1016/j.atmosenv.2005.10.051
  33. Mallants, D., and Feyen, J. (1990). "Defining homogeneous precipi- tation regions by means of principal components analysis." Journal of Applied Meteorology, Vol. 29, No. 9, pp. 892-901. https://doi.org/10.1175/1520-0450(1990)029<0892:DHPRBM>2.0.CO;2
  34. Mangiameli, P., Chen, S. K., and West, D. (1996). "A comparison of SOM neural network and hierarchical clustering methods." European Journal of Operational Research, Vol. 93, No. 2, pp. 402-417. https://doi.org/10.1016/0377-2217(96)00038-0
  35. Moon, S. W. (2006). A study on the performance of clustering methods using neural networks. Master dissertation, Yonsei University, Seoul, South Korea.
  36. Nam, W. S., Kim, T. S., Shin, J.-Y., and Heo, J.-H. (2008). "Regional rainfall frequency analysis by multivariate techniques." Journal of Korea Water Resources Association, Vol. 41, No. 5, pp. 517-525. https://doi.org/10.3741/JKWRA.2008.41.5.517
  37. Nam, W., Shin, H., Jung, Y., Joo, K., and Heo, J. H. (2015). "Delineation of the climatic rainfall regions of South Korea based on a multivariate analysis and regional rainfall frequency analyses." International Journal of Climatology, Vol. 35, No. 5, pp. 777-793. https://doi.org/10.1002/joc.4182
  38. Reusch, D. B., Alley, R. B., and Hewitson, B. C. (2007). "North atlantic climate variability from a self-organizing map perspective." Journal of Geophysical Research: Atmospheres, Vol. 112, No. D2.
  39. Smithers, J., and Schulze, R. (2001). "A methodology for the estimation of short duration design storms in South Africa using a regional approach based on L-moments." Journal of Hydrology, Vol. 241, No. 1, pp. 42-52. https://doi.org/10.1016/S0022-1694(00)00374-7
  40. Stedinger, J. R. (1983). "Estimating a regional flood frequency distribution." Water Resources Research, Vol. 19, No. 2, pp. 503-510. https://doi.org/10.1029/WR019i002p00503
  41. Su, M.-C., Liu, T.-K., and Chang, H.-T. (2002). "Improving the self- organizing feature map algorithm using an efficient initialization scheme." Tamkang Journal of Science and Engineering, Vol. 5, No. 1, pp. 35-48.
  42. Tian, J., Azarian, M. H., and Pecht, M. (2014). "Anomaly detection using self-organizing maps-based K-nearest neighbor algorithm." Proceedings of the European Conference of the Prognostics and Health Management Society.