Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
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Development of Mass Proliferation Control Algorithm of Phytoplankton Using Artificial Neural Network

인공신경망을 이용한 식물플랑크톤의 대량 증식 제어 알고리즘 개발

  • Seonghwa Park (Dept. of Civil & Environmental Engineering, Kunsan National University) ;
  • Jonggu Kim (Dept. of Environmental Engineering, Kunsan National University) ;
  • Minsun Kwon (Ocean Physics Dept., Land & Ocean Environmental Eng.)
  • 박성화 (군산대학교 토목환경공학부 ) ;
  • 김종구 (군산대학교 환경공학과 ) ;
  • 권민선 (국토해양환경기술단 )
  • Received : 2023.07.26
  • Accepted : 2023.08.29
  • Published : 2023.08.31
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Abstract

Suitable environmental conditions in Saemangeum frequently favor phytoplankton growth. There have been occurrences of sudden phytoplankton blooms, surpassing the algae management standards. A model was designed to prevent such blooms using scientific predictive techniques to forecast and regulate the possibility of phytoplankton blooms. We propose effective and efficient algae control measures concerning every phytoplankton species optimized through the policy control of nutrients (DIN, PO4-P) from rivers and controlling lake salinity using gate operations. The probability of phytoplankton blooms was initially forecast using an artificial neural network algorithm based on observations. The model's Kappa number fluctuated from 0.7889 to 1.0000, indicating good to excellent predictive power. The Garson algorithm was then utilized to assess the significance of explanatory variables for every species. Meanwhile, the probability of phytoplankton blooms was anticipated depending on the DIN and salinity value changes. Therefore, the model predicted the precise DIN and salinity concentrations to inhibit phytoplankton blooms for each species. Hence, the green algae model can create effective proactive measures to avoid future phytoplankton blooms in enormous artificial lakes.

새만금 내에서는 종종 식물플랑크톤이 증식하기에 알맞은 환경조건이 생성되며 일시에 식물플랑크톤 대증식이 발생하면서 조류 관리기준을 초과하는 사례가 발생하고 있다. 이를 대비하기 위하여 과학적 예측기법을 토대로, 식물플랑크톤의 종별로 가장 효과적이고 효율적인 녹조발생 억제 방안을 제안하기 위하여 식물플랑크톤 대증식 가능성을 예측하고, 제어할 수 있는 모델을 개발하였다. 즉, 하천에서 유입하는 영양염(DIN, PO4-P)을 정책적으로 조절하고, 갑문운영을 통해 호 내 염분을 제어하는 것이다. 먼저 관측치로부터 인공신경망 알고리즘을 이용해 식물플랑크톤 대증식 가능성을 예측 결과, 모델의 Kappa 수는 0.7889 ~ 1.0000의 범위로, good ~ excellent 수준이었다. 다음으로 Garson 알고리즘을 이용하여 종별로 설명변수의 중요도를 평가하였고, 또한 DIN 및 염분 값의 변화에 따른 식물플랑크톤 대량 증식 확률을 예측하였다. 그 결과, 각 종별로 식물플랑크톤의 대증식을 억제할 수 있는 DIN과 염분 농도를 정량적으로 예측할 수 있었다. 따라서, 향후 새만금과 같은 거대한 인공 호수에서 식물플랑크톤의 대증식을 억제하기 위한 효율적이고 효과적인 대응방안을 마련할 수 있도록 녹조제어모델을 활용할 수 있을 것으로 판단된다.

Keywords

References

  1. Ahn, S. J., I. S. Yeon, Y. S. Han, and J. K. Lee(2001), Water quality forecasting at Gongju station in Geum River using neural network model., Journal of the Korean Society of Civil Engineers, Vol. 34, No. 5, pp. 701-711. 
  2. Ahn, S. J., K. W. Jun, and K. I. Kim(2000), Forecastion of Runoff Hydrograph Usiong Neural Network Algorithms, Journal of Korea Water Resources Association, Vol. 33, No. 4. 
  3. Cho, Y. J., I. S. Yeon, and J. K. Lee(2004), Application of neural network model to the real-time forecasting of water quality, Journal of Korean Society on Water Quality, Vol. 18, No. 4, pp. 321-326. 
  4. Choi, J. K., J. H. Min, and D. W. Kim(2015), Three-dimensional Algal Dynamics Modeling Study in Lake Euiam Based on Limited Monitoring Data. Journal of Korean Society on Water Environment, 31(2), pp. 181-195.  https://doi.org/10.15681/KSWE.2015.31.2.181
  5. Codd, G. A., L. F. Morrison, and J. S. Metcalf(2005), Cyanobacterial Toxins: Risk Management for Health Protection, Toxicology and Applied Pharmacology, 203, pp. 264-272.  https://doi.org/10.1016/j.taap.2004.02.016
  6. Dencheva, K.(2010), State of Macrophytobenthic Communities and Ecological Status of the Varna Bay, Varna Lakes and Burgas Bay, Phytologia Balcanica, 16(1), pp. 43-50. 
  7. Garson, G. D.(1991), Interpreting Neural Network Connection Weights. AI Expert, 6, pp. 47-51. 
  8. Jang, K. G., J. W. Park, J. H. Park, N. Ha, and W. H. Yih(2009), Drastic Change of Phytoplankton Community at the Station "Mankyeong Bridge" of the New Saemankeum Lake during 2006-2007. Ocean and Polar Research, 31(1), pp. 71-76.  https://doi.org/10.4217/OPR.2009.31.1.071
  9. Jun, H. B., Y. J. Lee, B. D. Lee, and C. J. An(2001), Effects of the Ratio of Diatoms Length to the Effective Size of Filter Medium on Filter Clogging, Journal of the Korean Geo-Environmental Society, 2(1), pp. 31-35. [Korean Literature] 
  10. Karula, C., S. Soyupaka, A. F. Cilesizc, N. Akbayb, and E. Germenb(2000), Case studies on the use of neural networks in eutrophication modeling., J. Ecological Modelling, Vol. 134, pp. 145-152.  https://doi.org/10.1016/S0304-3800(00)00360-4
  11. Kim, H. J., J. Y. Park, and C. H. Moon(2018), Phytoplankton Spring Bloom and Environmental Factors in the Southern East Sea, Korea. THE JOURNAL OF FISHERIES AND MARINE SCIENCES EDUCATION, 30(1), pp. 19-27.  https://doi.org/10.13000/JFMSE.2018.02.30.1.19
  12. Kim, K. T., E. S. Kim, S. S. Kim, J. S. Park, J. K. Park, and S. R. Cho(2009), Water Quality and Heavy Metals in the Surface Seawaters of the Saemangeum Area during the Saemangeum-dike Construction. Journal of the Korean Society for Marine Environment & Energy, 12(1), pp. 35-46. 
  13. Lee, C. S., C. Y. Ahn, H. J. La, S. H. Lee, and H. M. Oh(2013), Technical and Strategic Approach for the Control of Cyanobacterial Bloom in Fresh Waters, Korean Journal of Environment Biology, 31(4), pp. 233-242.  https://doi.org/10.11626/KJEB.2013.31.4.233
  14. Lee, E. H. and D. I. Seo(2002), Water Quality Modelling of the Keum River - Effect of Yongdam Dam, Journal of Korea Water Resources Association, 35(5), pp. 525-539.  https://doi.org/10.3741/JKWRA.2002.35.5.525
  15. Lee, E. J., E. H. Na, and K. H. Kim(2012a), Development of a Water Quality Forecasting System for a Preventive Water Quality Management, Rural Resources, 54(1), pp. 50-55. 
  16. Lee, J. J., I. C. Choi, J. H. Yoon, S. H. Hong, S. Y. Yang, and Y. J. Lee(2012b), Report of Phytoplankton Alert System in the Daechung and Boryung Reservoirs, 11-1480523-001495-01, Guem River Environment Research Center, pp. 1-84. [Korean Literature] 
  17. Lehman, P. W., G. Boyer, C. Hall, S. Waller, and K. Gehrts (2005), Distribution and Toxicity of a New Colonial Microcystis aeruginosa Bloom in the San Francisco Bay Estuary, California, Hydrobiologia, 541, pp. 87-99.  https://doi.org/10.1007/s10750-004-4670-0
  18. Li, J., F. L. Peng, D. B. Ding, S. B. Zhang, D. L. Li, and T. Zhang(2011), Characteristics of the Phytoplankton Community and Bioaccumulation of Heavy Metals during Algal Blooms in Xiangjiang River, Science China Life Sciences, 54, pp. 931-938.  https://doi.org/10.1007/s11427-011-4222-6
  19. Oh, C. R., S. C. Park, H. M. Lee, and Y. P. Pyo(2002), A forecasting of water quality in the Youngsan River using neural network., Journal of the Korean Society of Civil Engineers, Vol. 35, No. 5, pp. 525-539.  https://doi.org/10.12652/KSCE.2015.35.2.0525
  20. Oh, C. R., Y. H. Jin, D. R. Kim, and S. C. Park(2008), Study on development of artificial neural network forecasting model using runoff, water quality data., Journal of Korea Water Resources Association, Vol. 41, No. 10, pp. 1035-1044.  https://doi.org/10.3741/JKWRA.2008.41.10.1035
  21. Park, S. C. and S. J. Ha(2003), Forecasting the water quality of river using GANN., Journal of the Korean Society of Civil Engineers, Vol. 23, No. 6B, pp. 507-514. 
  22. Park, S. H., J. G. Kim, G. O. Myung, and M. S. Kwon(2023), Relationship between phytoplankton distribution and salinity in the giant artificial brackish Lake Saemangeum, Ecohydrology & Hydrobiology, Vol. 23, pp. 251-260.  https://doi.org/10.1016/j.ecohyd.2023.01.003
  23. Recknagel, F., M. French, P. Harkonen, and K. I. Yabunaka (1994), Artificial neural network approach for modeling and prediction of algal blooms., J. Ecological Modelling, Vol. 96, pp. 11-28.  https://doi.org/10.1016/S0304-3800(96)00049-X
  24. Singh, K. P., A. Basant, A. Malik, and G. Jain(2009), Artificial neural network modeling of the river water quality-a case study, J. Ecological Modelling, Vol. 220, pp. 888-895.  https://doi.org/10.1016/j.ecolmodel.2009.01.004
  25. Watson, S. B.(2004), Aquatic Taste and Odor: A Primary Signal of Drinking-Water Integrity, Journal of Toxicology and Environmental Health, Part A 67, pp. 1779-1795.  https://doi.org/10.1080/15287390490492377
  26. Wilson, H. and F. Recknagel(2001), Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes., J. Ecological Modelling, Vol. 146, pp. 69-84.  https://doi.org/10.1016/S0304-3800(01)00297-6
  27. Yeo, H. G.(2010), Diversity of planktonic Micro Algae in Saemangeum Water Regions, Journal of the Korea Academia-Industrial Cooperation Society, Vol. 11, No. 9, pp. 3610-3614.  https://doi.org/10.5762/KAIS.2010.11.9.3610
  28. Yeo, H. G.(2012), Annual Variations (2001-2010) of Phytoplankton Standing Stocks in Saemangeum Water Region, Journal of the Korea Academia-Industrial cooperation Society, 13(9), pp. 4326-4333. https://doi.org/10.5762/KAIS.2012.13.9.4326