참고문헌 (22)

1. Ahn, S., Yeon, I., Han, Y. and Lee, J. (2001). Water quality forecasting at Gongju station in Geum River using neural network model. Journal of Korea Water Resources Association, 34, 701-711. 

2. Baden, S.P., Loo, L.O., Pihl, L. and Rosenberg, R. (1990). Effects of eutrophication on benthic communities including fish: Swedish west coast. AMBIO A Journal of the Human Environment, 13(3), 113-122. 

3. Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. 

   상세보기

4. Breitburg, D.L., Steinberg, N., Dubeau, S., Cooksey, C. and Houde, E.D. (1994). Effects of low dissolved oxygen on predation on estuarine fish larvae. Mar. Ecol. Prog. Ser., 104, 235-246. 

   상세보기

5. Diaz, R.J. and Solow, A. (1999). Ecological and economic consequences of hypoxia: topic 2 report for the integrated assessment on hypoxia in the Gulf of Mexico. NOAA Coastal Ocean Program Decision Analysis Series No. 16. National Oceanic and Atmospheric Administration, Coastal Ocean Office, Silver Spring, MD. 

6. Dupond, S. (2019). A thorough review on the current advance of neural network structures. Annual Reviews in Control, 14, 200-230. 

7. Eze, E. and Ajmal, T. (2020). Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach. Appl. Sci., 10(20), 7079. 

   상세보기

8. Hochreiter, S. and Schmidhuber J. (1997). Long short-term memory. Neural Computation, 9, 1735-1780. 

   상세보기

9. Huck, P.M. and Farquhar, G.J. (1974). Water quality models using the Box-Jenkins method. Journal of the Environmental Engineering, 100, 733-751. 

10. Jang, S.T. and Kim, K.C. (2006). Change of Oceanographic Environment in the Nakdong Estuary. Journal of the Korean Society of Oceanography, 11(1), 11-20. 

11. KOEM (Korea Marine Environment Management Corporation), Monitoring the marine environment, https://www.koem.or.kr/site/koem/main.do, 2020. 

12. K-water. (1996). A study on the prediction of pollutant advection and dispersion and on the reduction countermeasures in a large river: Focusing on the main stream of Nakdong River. K-water, Korea. 

13. Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J. and Guo, Y. (2021). Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Information Processing in Agriculture, 8(1), 185-193. 

    상세보기

14. Lim, H., An, H., Choi, E. and Kim, Y. (2020). Prediction of the DO concentration using the machine learning algorithm: case study in Oncheoncheon, Republic of Korea. Korean Journal of Agricultural Science, 47, 1029-1037. 

    원문보기 상세보기

15. MEIS (Marine Environmental Information System), Marine Environment Observation & Survey, https://www.meis.go.kr/mei/observe/port.do, 2020. 

16. Paerl, H.W., Pinckney, J.L., Fear, J.M. and Peierls, B.L. (1998). Ecosystem Responses to Internal and Watershed Organic Matter Loading: Consequences for Hypoxia in the Eutrophying Neuse River Estuary, North Carolina, USA. Marine Ecology Progress Series, 166, 17-25. 

    상세보기

17. Rosenberg, R., Elmgren, R., Fleischer, S., Jonsson, P., Persson, G. and Dahlin, H. (1990). Marine eutrophication case studies in Sweden: a synopsis. Ambio, 19, 102-108. 

18. Rumelhart, D., Hinton, G. and Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. 

    상세보기

19. Song, K.O., Ahn, O.S. and Park, C.K. (1993). Water quality modeling in the Nakdong river (2) - A study on DO balance. Korean Society on Water Environment, 9(1), 54-66. 

20. Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal, 3(2), 334-340. 

    상세보기

21. Yin, K., Lin, Z. and Ke, Z. (2004). Temporal and spatial distribution of dissolved oxygen in the Pearl River Estuary and adjacent coastal waters. Continental Shelf Research, 24(16), 1935-1948. 

    상세보기

22. Zhou, G.R. (2004). Theory of statistics. 2nd Ed. Nankai University Press, Tianjin. 

저자의 다른 논문 :

김경회 (18)