한국수자원학회논문집 (Journal of Korea Water Resources Association)

한국수자원학회 (Korea Water Resources Association)
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하천의 지표 미생물 모의를 위한 인공신경망 최적화

Optimum conditions for artificial neural networks to simulate indicator bacteria concentrations for river system

  • 배헌균 (계명대학교 환경학부 지구환경학전공)
  • Bae, Hun Kyun (Department of Global Environment, School of Environment, Keimyung University)
  • 투고 : 2021.09.27
  • 심사 : 2021.10.20
  • 발행 : 2021.12.31
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초록

현행 수질모니터링은 현장에서 수질 시료를 채취하여 실험실로 이동 후 분석하는 방법에 의존하고 있다. 이러한 분석기법은 노동집약적이며 경제적으로도 많은 부담이 주어진다. 그러나 현행 모니터링시스템을 개선하기 위하여 보다 짧은 시료채취주기 또는 시료채취지점 확대 등과 같은 방법을 동원하는 것은 인력 및 경제적 측면을 고려할 때 현실적으로 거의 불가능에 가깝다. 따라서 인력 및 경제적인 측면에서 큰 부담없이 현행 수질모니터링기법을 보완할 수 있는 방안이 고려되어야 한다. 본 연구에서는 모델링 기법을 도입하여 현행 수질모니터링 시스템을 보완하고자 하였고 인공신경망모델을 적용하였다. 인공신경망은 사람의 뇌에서 일어나는 작용을 모방한 기법으로 인지할 수 있는 현상을 뇌가 종합적으로 판단하는 과정을 컴퓨터에서 구현하는 방식인데 수질 예측을 위해 이러한 인공신경망기법을 도입 하였다. 본 연구에서는 수질 인자 중 Total coliform 을 타겟으로 하여 하천말단부에서 이들 인자를 예측할 수 있는지에 중점을 두고 연구를 수행하였다. 연구결과 제한된 입력인자를 이용하여 모델을 검보정하였음에도 불구하고 좋은 예측 성능을 보였다. 따라서 본 연구에서 사용된 기법을 근거로 수질상태를 사전에 예측함으로 수계 관리를 수행한다면 현 수질모니터링 시스템 보완에 큰 도움일 될 것으로 기대된다.

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

키워드

과제정보

본 연구는 한국환경기술개발원의 연구과제(과제번호 202000286001)의 지원 하에 이루어졌습니다.

참고문헌

  1. Bae, H.K. (2007). Modeling approaches to predict conditions of water quality using physical, chemical, and hydrological data focused on biological contaminations. Ph.D. dissertation, University of California, Irvine, CA, U.S., p.102.
  2. Boehm, A.B., Grant, S.B., Kim, J.H., Mowbray, S.L., McGee, C.D., Clark, C.D., Foley, D.M., and Wellman, D.E. (2002). "Decadal and shorter period variability of surf zone water quality at Huntington Beach, California." Environment Science and Technology, ACS Publications, Vol. 36, No. 18, pp. 3885-3892. https://doi.org/10.1021/es020524u
  3. Boudaghpour, S., Moghadam, H.S.A., Hajbabaie, M., Toliati, S.H. (2019). "Estimating chlorophyll-A concentration in the Caspian Sea from MODIS images using artificial neural networks." Environmental Engineering Research, KSEE, Vol. 25, No. 4, pp. 515-521. https://doi.org/10.4491/eer.2019.106
  4. Corrigan, J.A., Butkus, S.R., Ferris, M.E., and Roberts, J.C. (2021). "Microbial source tracking approach to investigate fecal waste at the Strawberry Creek watershed and Clam Beach, California, USA." International Journal of Environmental Research and Public Health, Vol. 18, No. 13, p. 6901. https://doi.org/10.3390/ijerph18136901
  5. French, C.B. (2003). Modeling Nitrogen transport in the Newport Bay/San Diego Creek watershed. Master Thesis, University of California Riverside, RIversidem, CA, U.S., pp. 24-25.
  6. Hsu, K.-L. Gupta, H.V., Gao, X., Sorooshian, S., and Imam, B. (2002). "Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis". Water Resources Research. Vol. 38, No. 12, pp. 1-17.
  7. Kamer, K., Schiff, K., Kennison, R.L., and Fong P. (2002). Macro-algal nutrient dynamics in upper Newport Bay. Technical Report, Southern California Coastal Water Research Project, CA, U.S., p. 33.
  8. Natural Resources Defense Council (NRDC) (2021). U.S., accessed 15 September 2021, .
  9. Oftelie, S., Saltzstein, A., Gianos, P., Boyum, K., Rocke, R., and Mosallem, A. (2000). Infrastructure: Latest survey finds orange county voters broadly similar to national survey respondents on the priority of cleaning up coastal waters. Technical Report, The Orange County Business Council, CA, U.S., p. 73.
  10. Reeves, R.L., Grant, S.B., Mrse, R.D., Copil-Oancea, C.M., Sanders, B.F., and Boehm, A.B. (2004). "Scaling and management of fecal indicator bacteria in runoff from a Coastal Urban Watershed in Southern California." Environment Science and Technology, ACS Publications, Vol. 38, No. 9, pp. 2637-2648. https://doi.org/10.1021/es034797g
  11. Searcy, R.T., and Boehm, A.B. (2021). "A Day at the beach: Enabling Coastal water quality prediction with high-frequency sampling and data-driven models." Environment Science and Technology, ACS Publications, Vol. 55, No. 3, pp. 1908-1918. https://doi.org/10.1021/acs.est.0c06742
  12. State Water Resources Control Board, California Environmental Protection Agency (SWRCB) (2001). Source investigations of storm drain discharges causing exceedances of bacteriological standards. U.S., p. 17.
  13. Strauss, A. (2002). Total maximum daily loads for toxic pollutants San Diego Creek and Newport Bay, California, U.S. Environmental Protection Agency Region 9, Washington DC, U.S., pp. 37
  14. Surfrider Foundation (2021). U.S., accessed 22 September 2021, .
  15. U.S. Envrionment Protection Agency (U.S. EPA) (2021). U.S., accessed 7 September 2021, .
  16. U.S. Fish & Wildlife Service (USFW) (2021). U.S., accessed 13 September 2021, .
  17. Vijayashanthar, V., Qiao, J., Zhu, Q., Entwistle, P., and Yu, G. (2018). "Modeling fecal indicator bacteria in urban waterways using artificial neural networks." Journal of Environmental Engineering, ASCE, Vol. 144, No. 6, doi: 10.1061/(ASCE)EE.1943-7870.0001377.
  18. Xu, T., Coco, G., and Neale, M. (2020). "A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning." Water Research, Elsevier, Vol. 177, No. 6, pp. 115788-115799. https://doi.org/10.1016/j.watres.2020.115788
  19. Zhang, J., Qiu, H., Li, X., Niu, J., Nevers, M.B., Hu, X., and Phanikumar, M.S. (2018). "real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach." Environment Science and Technology, ACS Publications, Vol. 52, No. 15, pp. 8446-8455. https://doi.org/10.1021/acs.est.8b01022