Acknowledgement
이 논문은 부경대학교 자율창의학술연구비(2023년)에 의하여 연구되었음
References
- Blum, A.L., and Langley, P. (1997). Selection of relevant features and examples in machine learning, Artif. Intell., 97, 245-271. https://doi.org/10.1016/S0004-3702(97)00063-5
- Choi, B. (2007). Influence of conditions for pre-treatment, aeration intensity and methods of backwash on micro-filter fouling in water treatment, Master's Thesis, Yonsei University.
- Dalmau, M., Rodriguez-Roda, I., Ayesa, E., Odriozola, J., Sancho, L., and Comas, J. (2013). Development of a decision tree for the integrated operation of nutrient removal MBRs based on simulation studies and expert knowledge, J. Chem. Eng., 217, 174-184. https://doi.org/10.1016/j.cej.2012.11.060
- Dong-Ah Geological Engineering (2012). Assessment of applicability by ceramic membrane filtration process at Beomeosa water treatment plant, Research report.
- Hall, M.A., and Smith, L.A. (1998). Practical feature subset selection for machine learning, Comput. Sci., 98, 181-191.
- Han, S.W., Lee, C.W., and Kang, L.S. (2003). Improvement of the effectiveness of drinking water treatment using a mixture of polyamine and PACl, Korean Chem. Eng. Res., 41(3), 319-325.
- Jang, D. and Park, M. (2021). Art price prediction using decision tree-based machine learning methods, Korean Manag. Rev., 50(2), 357-381. https://doi.org/10.17287/kmr.2021.50.2.357
- Kim, D., Kim, N., Jeon, J., Kim, J., Lim, J., and Kim S. (2022). Reconsidering clean-in-place criterion for low pressure membrane filtration systems using a model verified by long-term pilot plant operation data, J. Water Process. Eng., 46, 102506.
- Kim, J.Y., Jeon, J., Kim, N., and Kim, S. (2021). Predicting flux of forward osmosis membrane module using deep learning, J. Korean Soc. Water Wastewater, 35(1), 93-100. https://doi.org/10.11001/jksww.2021.35.1.093
- Kim, M., Kim, N., Jeon, J., and Kim, S. (2020), Vibration signals at the early stage of fouling in reverse osmosis system, Desalination Water Treat., 183, 81-87. https://doi.org/10.5004/dwt.2020.25257
- Kim, S. (2013). A process diagnosis method for membrane water treatment plant using a constant flux membrane fouling model, J. Korean Soc. Water Wastewater, 27(1), 139-146. https://doi.org/10.11001/jksww.2013.27.1.139
- Kim, S., Lim, J., Park, J.Y., and Kim, J.O. (2014). Effect of flux fluctuation on the fouling in membrane water treatment system for smart water grid, Desalination Water Treat., 52, 1028-1034. https://doi.org/10.1080/19443994.2013.826775
- Ko, Y.S. (2011). The Construction methodology of a rule-based expert system using CART-based decision tree method, J. Korea Inst. Electron. Commun. Sci., 6(6), 849-854.
- Park, S.H., Park, Y.G., Lim, J., and Kim, S. (2015). Evaluation of ceramic membrane applications for water treatment plants with a life cycle cost analysis, Desalination Water Treat., 54, 973-979. https://doi.org/10.1080/19443994.2014.912162
- Pi, M.G., Shin, I.H., and Min, O.G. (2019). Visual analytics system to help feature selection of machine learning, Korea Software Congress 2019, Pyeongchang, South korea.
- Schmitt, F., Banu, R., Yeom, I.T., and Do, K.U. (2018). Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater, Biochem. Eng. J., 133, 47-58. https://doi.org/10.1016/j.bej.2018.02.001
- Shetty, G.R., and Chellam, S. (2003). Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks, J. Membr. Sci., 217(1), 69-86. https://doi.org/10.1016/S0376-7388(03)00075-9
- Song, Y.Y. and Lu, Y. (2015). Decision tree methods: applications for classification and prediction, Shanghai Arch. Psychiatry, 27(2), 130-135.
- Yoon, N., Kim, J., Lim, J.L., Abbas, A., Jeong, K., and Cho, K.H. (2021). Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant, Desalination, 512, 115107.
- You, S.J., Ahan, H.W., Park, S.H., Lim, J., Hong, S.C., and Lee, B.I. (2014). The study on optimum operation conditions of ceramic MF membrane process in Y water treatment plant, Membr., 24, 201-212. https://doi.org/10.14579/MEMBRANE_JOURNAL.2014.24.3.201