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[해외논문] Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake

Journal of hydrology, v.602, 2021년, pp.126817 -   

Kim, Jaeyoung ,  Seo, Dongil ,  Jang, Miyoung ,  Kim, Jiyong

초록이 없습니다.

참고문헌 (63)

  1. Journal of Hydrology Ahmed 578 2019 Machine learning methods for better water quality prediction 

  2. Ecological Modelling Bae 372 53 2018 10.1016/j.ecolmodel.2018.01.019 Analysis and Modeling of Algal Bloom Occurrences in the Nakdong River 

  3. 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 

  4. Bartram 1996 Water quality monitoring: a practical guide to the design and implementation of freshwater quality studies and monitoring programmes 

  5. 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 

  6. IEEE Transactions on Signal Processing Benvenuto 40 4 967 1992 10.1109/78.127967 On the complex backpropagation algorithm 

  7. Journal of machine learning research Bergstra 13 2 2012 Random search for hyper-parameter optimization 

  8. Bicknell No. EPA/600/R-97/080 1997 

  9. EPA Bowie 600 3 1985 Rates, constants, and kinetics formulations in surface water quality modeling 

  10. 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. 

  11. Chapra 2008 Surface water-quality modeling 

  12. Applied Sciences Chen 10 17 5776 2020 10.3390/app10175776 A review of the artificial neural network models for water quality prediction 

  13. 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 

  14. IEEE access Cloete 4 3975 2016 10.1109/ACCESS.2016.2592958 Design of smart sensors for real-time water quality monitoring 

  15. Mathematische annalen Courant 100 1 32 1928 10.1007/BF01448839 Über die partiellen Differenzengleichungen der mathematischen Physik 

  16. 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. 

  17. 10.2174/1874378101105010026 Daniel, E.B., Camp, J.V., LeBoeuf, E.J., Penrod, J.R., Dobbins, J.P., Abkowitz, M.D., 2011. Watershed modeling and its applications: A state-of-the-art review. The Open Hydrology Journal 5(1) 10.2174/1874378101105010026. 

  18. Water resources research Edinger 4 5 1137 1968 10.1029/WR004i005p01137 The response of water temperatures to meteorological conditions 

  19. 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. 

  20. SIAM Journal on Numerical Analysis Fritsch 17 2 238 1980 10.1137/0717021 Monotone piecewise cubic interpolation 

  21. 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 

  22. Hamrick 1992 A three-dimensional environmental fluid dynamics computer code: Theoretical and computational aspects 

  23. 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 

  24. Environmental Modelling & Software Hirsch 73 148 2015 10.1016/j.envsoft.2015.07.017 A bootstrap method for estimating uncertainty of water quality trends 

  25. 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 

  26. Ji 2017 Hydrodynamics and water quality: modeling rivers, lakes, and estuaries 

  27. Ecological Modelling Katin 447 2021 10.1016/j.ecolmodel.2021.109497 Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity 

  28. Journal of Hydrology: Regional Studies Kim 33 2021 Factors affecting harmful algal bloom occurrence in a river with regulated hydrology 

  29. 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 

  30. Köster 2003 Analytical methods for microbiological water quality testing 

  31. Talanta Kovacs 147 598 2016 10.1016/j.talanta.2015.10.024 Water spectral pattern as holistic marker for water quality monitoring 

  32. Science Lazer 343 6176 1203 2014 10.1126/science.1248506 The parable of Google Flu: traps in big data analysis 

  33. 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 

  34. 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 

  35. 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 

  36. Water Resource Systems Planning and Management Loucks 417-467 2017 10.1007/978-3-319-44234-1_10 Water quality modeling and prediction 

  37. Neural computation MacKay 4 3 415 1992 10.1162/neco.1992.4.3.415 Bayesian interpolation 

  38. Artificial neural networks in hydrology Maier 287-309 2000 10.1007/978-94-015-9341-0_15 Application of artificial neural networks to forecasting of surface water quality variables: issues, applications and challenges 

  39. 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 

  40. Minsky 1969 An introduction to computational geometry 

  41. Towards global optimization Mockus 2 117-129 2 1978 The application of Bayesian methods for seeking the extremum 

  42. Montgomery 2017 Design and analysis of experiments 

  43. 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 

  44. Neitsch 2011 Soil and water assessment tool theoretical documentation version 2009 

  45. 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 

  46. 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. 

  47. 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 

  48. 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 

  49. Reichert, P., Borchardt, D., Henze, M., Rauch, W., Shanahan, P., Somlyody, L., Vanrolleghem, P.A., 2001. River water quality model. IWA publishing. 

  50. Reynolds 2006 The ecology of phytoplankton 

  51. 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. 

  52. 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 

  53. 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. 

  54. 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 

  55. Thomann 1987 Principles of surface water quality modeling and control 

  56. Ecological Modelling Tian 364 42 2017 10.1016/j.ecolmodel.2017.09.013 An optimization of artificial neural network model for predicting chlorophyll dynamics 

  57. 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 

  58. 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 

  59. 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. 

  60. 10.1007/978-94-017-3048-8_23 Willmott, C.J., 1984. On the evaluation of model performance in physical geography, Spatial statistics and models. Springer, pp. 443-460. 10.1007/978-94-017-3048-8_23. 

  61. 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 

  62. 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 

  63. 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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