Acknowledgement
본 연구는 2020년 한국연구재단의 이공분야기초연구사업 (NRF-2020R1I1A3A0403784313)의 재원으로 수행된 연구결과 중 일부임을 밝히며, 연구비 지원에 감사드립니다.
References
- Ahn, J.M., Lee, K. and Lyu, S. (2020). Effect of changes in watershed runoff characteristics on salinity intrusion in estuary using EFDC. KSCE J. Civ. Eng., 24(1), 87-98. https://doi.org/10.1007/s12205-020-1306-5
- Barzegar, R., Aalami, M.T. and Adamowski, J. (2020). Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model. Stochastic Environmental Research and Risk Assessment, 34(2), 415-433. https://doi.org/10.1007/s00477-020-01776-2
- Blumberg, A.F. and Mellor, G.L. (1987). A description of a three-dimensional coastal ocean circulation model. Three-dimensional Coastal Ocean Models, 4, 1-16. https://doi.org/10.1029/CO004p0001
- Chen, W., Liu, W., Huang, W. and Liu, H. (2017). Prediction of salinity variations in a tidal estuary using artificial neural network and three-dimensional hydrodynamic models. Comput. Water Energy Environ. Eng., 06, 107-128. https://doi.org/10.4236/cweee.2017.61009
- Han, C.-S., Park, S.-K., Jung, S.-W. and Roh, T.-Y. (2011). The study of salinity distribution at nakdong river estuary. J. Korean Soc. Coast. Ocean Eng., 23(1), 101-108 (in Korean). https://doi.org/10.9765/KSCOE.2011.23.1.101
- Hochreiter, S. and chmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Jeong, S., Lee, S., Hur, Y.T., Kim, Y. and Kim, H.Y. (2022). Development of seawater inflow equations considering density difference between seawater and freshwater at the Nakdong River estuary. J. Korea Water Resources Association, 55(5), 383-392 (in Korean).
- Kim, T. (2020). Study on th Behavior Analysis of Salt Water According to the Operation of Nakdong River Estuary Barrage. Inje University.
- Kim, T. and Lee, W.-D. (2022). Review on applications of machine learning in coastal and ocean engineering. J. Ocean Eng. Technol., 36(3), 194-210. https://doi.org/10.26748/KSOE.2022.007
- Lee, S.-M., Park, K.-D. and Kim, I.-K. (2020). Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong River (focusing on water quality and quantity factors). J. Korean Soc. Water Wastewater, 34, 277-288 (in Korean). https://doi.org/10.11001/jksww.2020.34.4.277
- Melesse, A.M., Khosravi, K., Tiefenbacher, J.P., Heddam, S., Kim, S., Mosavi, A. and Pham, B.T. (2020). River water salinity prediction using hybrid machine learning models. Water, 12, 2951.
- Park, S. and Kim, K. (2021). Prediction of DO concentration in nakdong river estuary through case study based on long short term memory model. J. Korean Soc. Coast. Ocean Eng., 33(6), 238-245 (in Korean). https://doi.org/10.9765/KSCOE.2021.33.6.238
- Parsa, J., Etemad-Shahidi, A. and Hosseiny, S. (2007). Evaluation of computer and empirical models for prediction of salinity intrusion in the bahmanshir estuary. J. Coast. Res., 50(SI), 658-662.
- Seo, I.W. and Yun, S.H. (2016). Forcasting water quality by ANN model at the downstream of cheongpyeong dam. KSCE 2016 CONVENTION PROGRAM, 41-42.
- Yang, H., Lee, K., Choo, Y. and Kim, K. (2020). Underwater acoustic research trends with machine learning: Ocean parameter inversion applications. J. Ocean Eng. Technol., 34(5), 371-376. https://doi.org/10.26748/KSOE.2020.016