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NTIS 바로가기스마트미디어저널 = Smart media journal, v.11 no.2, 2022년, pp.31 - 38
이호준 (동아대학교 컴퓨터AI공학부 컴퓨터공학과) , 조민규 (동아대학교 컴퓨터AI공학부 컴퓨터공학과) , 천세진 (동아대학교 컴퓨터AI공학부) , 한정규 (동아대학교 컴퓨터AI공학부)
Promptly predicting changes in the salinity in rivers is an important task to predict the damage to agriculture and ecosystems caused by salinity infiltration and to establish disaster prevention measures. Because machine learning(ML) methods show much less computation cost than physics-based hydrau...
낙동강유역물관리위, "낙동강 하구 기수생태계 복원방안 의결," 2022.
김상현, 이한범, 전성완, 김대연, 이상정, "LTSM 신경망을 이용한 당뇨병 입원환자의 혈당 예측," 정보과학회논문지, 제47권, 제12호, 1120-1125쪽, 2020년 12월
D. Won, S. Kim, Y. Kim, and G. Song, "Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods." Journal of KIISE, Vol. 48, No.7, pp.764-773, Jul. 2021.
S.K. Park, T.Y. Noh, D.H. Kim, and C.S. Han, "The Study of Salinity Distribution at Nakdong River Estuary," Proceedings of the 18th Korean Society of Coastal and Ocean Engineers Conference, pp. 109-112, Busan, Republic of Korea, Nov. 2009.
T. Rajkumar and M. L. Johnson, "Prediction of salinity in San Francisco bay delta using neural network," IEEE Int'l Conf. on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace, Vol. 1, pp. 329-334, 2001.
C. Qiu and Y. Wan, "Time series modeling and prediction of salinity in the Caloosahatchee River Estuary," Water Resources Research, Vol. 49, No. 9, pp. 5804-5816, Jul., 2013.
A. M. Melesse, K. Khosravi, J. P. Tiefenbacher, S. Heddam, S. Kim, A. Mosavi, and B. T. Pham, "River Water Salinity Prediction Using Hybrid Machine Learning Models," Water, Vol. 12, No. 10, pp. 2951-2971, Oct., 2020.
D. Tran, M. Tsujimura, N. Ha, V. Nguyen, D. Binh, T. Dang, Q. Doan, D. Bui, T. Ngoc, L. Phu, P. Thuc, and T. Pham, "Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam," Ecological Indicators, Vol. 127, Aug. 2021.
J. Hamrick, "A Three-Dimensional Environmental Fluid Dynamics Computer Code; Theoretical and Computational Aspects," Virginia Institute of Marine Science Special Report, May 1992.
Blumberg, A.F. "A primer for ECOMSED version 1.3 users manual," HydroQual, Inc., USA. Feb., 2002.
F. Murtagh, "Multilayer perceptrons for classifica tion and regression," Neurocomputing, Vol. 2, Issues. 5-6, pp. 183-197, Jul. 1991.
T. Chen and C. Guestrin. "XGBoost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785-794, San Francisco, USA, Aug. 2016.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree," Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3149-3157, Long Beach, USA. Dec., 2017.
From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science, http://uilis.unsyiah.ac.id/oer/items/show/139 (accessed Mar., 6, 2022)
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