DOI QR코드

DOI QR Code

Characteristics Detection of Hydrological and Water Quality Data in Jangseong Reservoir by Application of Pattern Classification Method

패턴분류 방법 적용에 의한 장성호 수문·수질자료의 특성파악

  • Park, Sung-Chun (Department of Civil Engineering, Dongshin University) ;
  • Jin, Young-Hoon (Department of Civil Engineering, Dongshin University) ;
  • Roh, Kyong-Bum (Startup Assistance Foundation, Mokpo National University) ;
  • Kim, Jongo (Department of Environmental Education, Mokpo National University) ;
  • Yu, Ho-Gyu (Jeollanamdo Provincial Office)
  • Received : 2011.07.15
  • Accepted : 2011.09.23
  • Published : 2011.11.30

Abstract

Self Organizing Map (SOM) was applied for pattern classification of hydrological and water quality data measured at Jangseong Reservoir on a monthly basis. The primary objective of the present study is to understand better data characteristics and relationship between the data. For the purpose, two SOMs were configured by a methodologically systematic approach with appropriate methods for data transformation, determination of map size and side lengths of the map. The SOMs constructed at the respective measurement stations for water quality data (JSD1 and JSD2) commonly classified the respective datasets into five clusters by Davies-Bouldin Index (DBI). The trained SOMs were fine-tuned by Ward's method of a hierarchical cluster analysis. On the one hand, the patterns with high values of standardized reference vectors for hydrological variables revealed the high possibility of eutrophication by TN or TP in the reservoir, in general. On the other hand, the clusters with low values of standardized reference vectors for hydrological variables showed the patterns with high COD concentration. In particular, Clsuter1 at JSD1 and Cluster5 at JSD2 represented the worst condition of water quality with high reference vectors for rainfall and storage in the reservoir. Consequently, SOM is applicable to identify the patterns of potential eutrophication in reservoirs according to the better understanding of data characteristics and their relationship.

Keywords

References

  1. 김범철, 사승환, 김문숙, 이윤경, 김재구(2007). 국내 호수의 제한영양소와 하수처리장 방류수 인 기준 강화의 필요성. 수질보전 한국물환경학회지, 23(4), pp. 512-517.
  2. 김용구, 진영훈, 정우철, 박성천(2008). 호소수의 강우, 저류량 및 TOC변동 특성분석을 위한 자기조직화 방법의 적용. 수질보전 한국물환경학회지, 24(5), pp. 611-617.
  3. 농림수산식품부, 한국농어촌공사(2010). 농업용저수지 둑 높이기사업 사전환경성검토서 (영산강수계: 장성호).
  4. 박유미, 이의행, 이상재, 안광국(2009). 탑정저수지의 부영양화 특성 및 주요 변수 간의 상호관계. 한국하천호수학회지, 42(3), pp. 382-393.
  5. 송자섭(2011), SOFM의 적용에 의한 영산강 수질 및 유량자료의 시.공간적 패턴분류 특성. 박사학위논문, 동신대학교.
  6. 이현준, 안광국(2009). 우리나라 인공호 관리를 위한 다변수 수질평가 모델의 개발 및 적용. 한국하천호수학회지, 42(2), pp. 242-252.
  7. 진영훈, 김용구, 노경범, 박성천(2009). 수질 및 유량자료의 기초통계량 분석에 따른 공간분포 파악을 위한 SOM의 적용. 수질보전 한국물환경학회지, 25(5), pp. 735-741.
  8. 한국농어촌공사(2010). 농업용저수지 유형별 수질예측모델 적용방안 연구.
  9. 환경부 국립환경과학원 물환경정보시스템(2011). http://water.nier.go.kr/.
  10. Aguilera, P. A., Frenich, A. G., Torres, J. A., Castro, H., Vidal, J. L. M., and Canton, M. (2001). Application of the Kohonen neural network in coastal water management: Methodological development for the assessment and prediction of water quality. Water Research, 35, pp. 4053-4062. https://doi.org/10.1016/S0043-1354(01)00151-8
  11. Alvarez-Guerra, M., Gonzalez-Pinuela, C., Andres, A., Galan, B., and Viguri, J. R. (2008). Assessement of Self-Organizing Map artificial neural networks for the classification of sediment quality. Environmental International, 34, pp. 782-790. https://doi.org/10.1016/j.envint.2008.01.006
  12. Bedoya, D., Novotny, V., and Manolakos, E. S. (2009). Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction. Ecological Modelling, 220, pp. 2393-2406. https://doi.org/10.1016/j.ecolmodel.2009.06.017
  13. Faggiano, L., Zwart, D., Garcia-Berthou, E., Lek, S., and Gevrey, M. (2010). Patterning ecological risk of pesticide contamination at the river basin scale. Science of the Total Environment, 408, pp. 2319-2326. https://doi.org/10.1016/j.scitotenv.2010.02.002
  14. Garcia, H. L. and Gonzalez, I. M. (2004). Self-organizing map and clustering for wastewater treatment monitoring. Engineering Applications of Artificial Intelligence, 17, pp. 215-225. https://doi.org/10.1016/j.engappai.2004.03.004
  15. Hentati, A., Kawamura, A., Amaguchci, H., and Iseri, Y. (2010). Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map. Geomorphology, 122, pp. 56-64. https://doi.org/10.1016/j.geomorph.2010.05.013
  16. Hsu, K. L., Gupta, H. V., Sorooshian, S., and Imam, B. (2002). SOLO-An artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research, 38, pp. 1-38.
  17. Ikem, A. and Adisa, S. (2011). Runoff effect on eutrophic lake water quality and heavy metal distribution in recent littoral sediment. Chemosphere, 82, pp. 259-267. https://doi.org/10.1016/j.chemosphere.2010.09.048
  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, DOI: 10.1016/j.ecoinf.2010.04.005.
  19. Kalteh, A. M., Hjorth, P., and Berndtsson, R. (2008). Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling and Software, 23, pp. 835-845. https://doi.org/10.1016/j.envsoft.2007.10.001
  20. Kazi, T. G., Arain, M. B., Jamali, M. K., Jalbani, N., Afridi, H. I., Sarfraz, R. A., Baig, J. A., and Shah, A. Q. (2009). Assessment of water quality of polluted lake using multivariate statistical techniques: A case study. Ecotoxicology and Environmental Safety, 72, pp. 301-309. https://doi.org/10.1016/j.ecoenv.2008.02.024
  21. Lu, H. C., Hsieh, J. C., and Chang, T. S. (2006). Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network. Atmospheric Research, 81, pp. 124-139. https://doi.org/10.1016/j.atmosres.2005.11.007
  22. Mustonen, S. M., Tissari, S., Huikko, L., Kolehmainen, M., Lehtola, M. J., and Hirvonen, A. (2008). Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods. Water Research, 42, pp. 2421-2430. https://doi.org/10.1016/j.watres.2008.01.015
  23. Park, Y. S., Song, M. Y., Park, Y. C., Oh, K. H., Cho, E., and Chon, T. S. (2007). Community patterns of benthic macroinvertebrates collected on the national scale in Korea. Ecological Modelling, 203, pp. 26-33. https://doi.org/10.1016/j.ecolmodel.2006.04.032
  24. Song, M. Y., Hwang, H. J., Kwak, I. S., Ji, C. W., Oh, Y. N., Youn, B. J., and Chon, T. S. (2007). Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecological Modelling, 203, pp. 18-25. https://doi.org/10.1016/j.ecolmodel.2006.04.027
  25. Su, S., Zhi, J., Lou, L., Huang, F., Chen, X., and Wu, J. (2010). Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques. Physics and Chemistry of the Earth, DOI: 10.1016/j.pce.2010.03.021.
  26. Tobiszewski, M., Tsakovski, S., Simeonov, V., and Namiesnik, J. (2010). Surface water quality assessment by the use of combination of multivariate statistical classification and expert information. Chemosphere, 80, pp. 740-746. https://doi.org/10.1016/j.chemosphere.2010.05.024
  27. Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J. (2000). SOMToolbox for Matlab5, Helsinki University of Technology Report A57.