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http://dx.doi.org/10.9765/KSCOE.2021.33.6.238

Prediction of DO Concentration in Nakdong River Estuary through Case Study Based on Long Short Term Memory Model  

Park, Seongsik (Department of Ocean Engineering, Pukyong National University)
Kim, Kyunghoi (Department of Ocean Engineering, Pukyong National University)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.33, no.6, 2021 , pp. 238-245 More about this Journal
Abstract
In this study, we carried out case study to predict dissolved oxygen (DO) concentration of Nakdong river estuary with LSTM model. we aimed to figure out a optimal model condition and appropriate predictor for prediction in dissolved oxygen concentration with model parameter and predictor as cases. Model parameter case study results showed that Epoch = 300 and Sequence length = 1 showed higher accuracy than other conditions. In predictor case study, it was highest accuracy where DO and Temperature were used as a predictor, it was caused by high correlation between DO concentration and Temperature. From above results, we figured out an appropriate model condition and predictor for prediction in DO concentration of Nakdong river estuary.
Keywords
deep learning; long short-term memory; Nakdong river; estuary; dissolved oxygen (DO); prediction;
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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.   DOI
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.   DOI
5 Dupond, S. (2019). A thorough review on the current advance of neural network structures. Annual Reviews in Control, 14, 200-230.
6 Eze, E. and Ajmal, T. (2020). Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach. Appl. Sci., 10(20), 7079.   DOI
7 Hochreiter, S. and Schmidhuber J. (1997). Long short-term memory. Neural Computation, 9, 1735-1780.   DOI
8 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.
9 KOEM (Korea Marine Environment Management Corporation), Monitoring the marine environment, https://www.koem.or.kr/site/koem/main.do, 2020.
10 Huck, P.M. and Farquhar, G.J. (1974). Water quality models using the Box-Jenkins method. Journal of the Environmental Engineering, 100, 733-751.
11 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.
12 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.   DOI
13 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.   DOI
14 MEIS (Marine Environmental Information System), Marine Environment Observation & Survey, https://www.meis.go.kr/mei/observe/port.do, 2020.
15 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.   DOI
16 Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal, 3(2), 334-340.   DOI
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.   DOI
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 Zhou, G.R. (2004). Theory of statistics. 2nd Ed. Nankai University Press, Tianjin.
21 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.
22 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.   DOI