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NTIS 바로가기Journal of hydrology, v.602, 2021년, pp.126817 -
Kim, Jaeyoung , Seo, Dongil , Jang, Miyoung , Kim, Jiyong
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Journal of Hydrology Ahmed 578 2019 Machine learning methods for better water quality prediction
Ecological Modelling Bae 372 53 2018 10.1016/j.ecolmodel.2018.01.019 Analysis and Modeling of Algal Bloom Occurrences in the Nakdong River
Ecological Modelling Bae 454 2021 10.1016/j.ecolmodel.2021.109590 Changes in algal bloom dynamics in a regulated large river in response to eutrophic status
Bartram 1996 Water quality monitoring: a practical guide to the design and implementation of freshwater quality studies and monitoring programmes
Science of the Total Environment Behmel 571 1312 2016 10.1016/j.scitotenv.2016.06.235 Water quality monitoring strategies-A review and future perspectives
IEEE Transactions on Signal Processing Benvenuto 40 4 967 1992 10.1109/78.127967 On the complex backpropagation algorithm
Journal of machine learning research Bergstra 13 2 2012 Random search for hyper-parameter optimization
Bicknell No. EPA/600/R-97/080 1997
EPA Bowie 600 3 1985 Rates, constants, and kinetics formulations in surface water quality modeling
Brochu, E., Cora, V.M., De Freitas, N., 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
Chapra 2008 Surface water-quality modeling
Applied Sciences Chen 10 17 5776 2020 10.3390/app10175776 A review of the artificial neural network models for water quality prediction
Choi 631 2019 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE Modelling chlorophyll-a concentration using deep neural networks considering extreme data imbalance and skewness
IEEE access Cloete 4 3975 2016 10.1109/ACCESS.2016.2592958 Design of smart sensors for real-time water quality monitoring
Mathematische annalen Courant 100 1 32 1928 10.1007/BF01448839 Über die partiellen Differenzengleichungen der mathematischen Physik
Craig, P., Chung, D., Lam, N., Son, P., Tinh, N., 2014. Sigma-zed: A computationally efficient approach to reduce the horizontal gradient error in the EFDC’s vertical sigma grid, Proceedings of the 11th International Conference on Hydrodynamics (ICHD 2014), Singapore, pp. 19-24.
Water resources research Edinger 4 5 1137 1968 10.1029/WR004i005p01137 The response of water temperatures to meteorological conditions
Foresee, F., Hagan, M., 1997. Gauss-Newton approximation to Bayesian learning. Proceedings of the 1997 international joint conference on neural networks, IEEE, 1930-1935. 10.1109/ICNN.1997.614194.
SIAM Journal on Numerical Analysis Fritsch 17 2 238 1980 10.1137/0717021 Monotone piecewise cubic interpolation
Ecological Modelling Guallar 338 37 2016 10.1016/j.ecolmodel.2016.07.009 Artificial neural network approach to population dynamics of harmful algal blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia
Hamrick 1992 A three-dimensional environmental fluid dynamics computer code: Theoretical and computational aspects
Journal of Japan Society of Hydrology and Water Resources He 19 4 249 2006 10.3178/jjshwr.19.249 Application of the artificial neural network method to estimate the missing hydrologic data
Environmental Modelling & Software Hirsch 73 148 2015 10.1016/j.envsoft.2015.07.017 A bootstrap method for estimating uncertainty of water quality trends
JAWRA Journal of the American Water Resources Association Hirsch 46 5 857 2010 10.1111/j.1752-1688.2010.00482.x Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay river inputs 1
Ji 2017 Hydrodynamics and water quality: modeling rivers, lakes, and estuaries
Ecological Modelling Katin 447 2021 10.1016/j.ecolmodel.2021.109497 Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity
Journal of Hydrology: Regional Studies Kim 33 2021 Factors affecting harmful algal bloom occurrence in a river with regulated hydrology
Ecological Modelling Kim 366 27 2017 10.1016/j.ecolmodel.2017.10.015 Algal bloom prediction of the lower Han River, Korea using the EFDC hydrodynamic and water quality model
Köster 2003 Analytical methods for microbiological water quality testing
Talanta Kovacs 147 598 2016 10.1016/j.talanta.2015.10.024 Water spectral pattern as holistic marker for water quality monitoring
Science Lazer 343 6176 1203 2014 10.1126/science.1248506 The parable of Google Flu: traps in big data analysis
International journal of environmental research and public health Lee 15 7 1322 2018 10.3390/ijerph15071322 Improved prediction of harmful algal blooms in four Major South Korea’s Rivers using deep learning models
Water resources research Legates 35 1 233 1999 10.1029/1998WR900018 Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation
Water Lepot 9 10 796 2017 10.3390/w9100796 Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment
Neural computation MacKay 4 3 415 1992 10.1162/neco.1992.4.3.415 Bayesian interpolation
Journal of atmospheric and oceanic technology Mellor 11 4 1126 1994 10.1175/1520-0426(1994)011<1126:TPGCOS>2.0.CO;2 The pressure gradient conundrum of sigma coordinate ocean models
Minsky 1969 An introduction to computational geometry
Towards global optimization Mockus 2 117-129 2 1978 The application of Bayesian methods for seeking the extremum
Montgomery 2017 Design and analysis of experiments
Atmospheric chemistry and physics Musial 11 15 7905 2011 10.5194/acp-11-7905-2011 Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series
Neitsch 2011 Soil and water assessment tool theoretical documentation version 2009
Expert systems with applications Paliwal 36 1 2 2009 10.1016/j.eswa.2007.10.005 Neural networks and statistical techniques: A review of applications
Park, K., Kuo, A.Y., Shen, J., Hamrick, J.M., 1995. A three-dimensional hydrodynamic-eutrophication model (HEM-3D): Description of water quality and sediment process submodels. 10.21220/V5ZH9N.
Ecosphere Peters 5 6 1 2014 10.1890/ES13-00359.1 Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology
Defence Technology Rabbath 15 5 741 2019 10.1016/j.dt.2019.07.016 A comparison of piecewise cubic Hermite interpolating polynomials, cubic splines and piecewise linear functions for the approximation of projectile aerodynamics
Reichert, P., Borchardt, D., Henze, M., Rauch, W., Shanahan, P., Somlyody, L., Vanrolleghem, P.A., 2001. River water quality model. IWA publishing.
Reynolds 2006 The ecology of phytoplankton
Rossman, L.A., 2010. Storm water management model user's manual, version 5.0. National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency.
Desalination and Water Treatment Seo 19 1-3 42 2010 10.5004/dwt.2010.1894 3-D hydrodynamic modeling of Yongdam Lake, Korea using EFDC
Shin, C.M., Na, E.H., Park, J.D., Park, J.H., Lee, S.W., Jeong, J.H., Rhew, D.H., Jeong, D.I., 2008. Application of Parameters and Coefficients of River Water Quality Model for TMDL Plan in Korea. National Institute of Environmental Research, NIERNO 2008-29-979.
Journal of environmental management Telci 90 10 2987 2009 10.1016/j.jenvman.2009.04.011 Optimal water quality monitoring network design for river systems
Thomann 1987 Principles of surface water quality modeling and control
Ecological Modelling Tian 364 42 2017 10.1016/j.ecolmodel.2017.09.013 An optimization of artificial neural network model for predicting chlorophyll dynamics
Environmental Science and Pollution Research Tomić 25 10 9360 2018 10.1007/s11356-018-1246-5 Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction
Neural Computing and Applications Wang 32 1 163 2020 10.1007/s00521-018-3790-9 An approach of recursive timing deep belief network for algal bloom forecasting
10.1080/02723646.1981.10642213 Willmott, C.J., 1981. On the validation of models. Physical geography 2(2), 184-194. 10.1080/02723646.1981.10642213.
Environmental Modelling & Software Zadeh 118 35 2019 10.1016/j.envsoft.2019.03.022 Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification
Applied Mathematical Modelling Zain 36 4 1477 2012 10.1016/j.apm.2011.09.035 Regression and ANN models for estimating minimum value of machining performance
Water Research Zhang 164 2019 10.1016/j.watres.2019.114888 Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network
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