The Journal of
the Korean Society on Water Environment

The Journal of
the Korean Society on Water Environment

Bimonthly
  • ISSN : 2289-0971 (Print)
  • ISSN : 2289-098X (Online)
  • KCI Accredited Journal

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  3. 2025-11 (Vol. 41, No. 6)
  4. 10.15681/KSWE.2025.41.6.539
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References

1 
Boretti A., Rosa L., 2019, Reassessing the projections of the world water development report, npj Clean Water, Vol. 2, pp. 15DOI
2 
Cifuentes-Cabezas M., Bohórquez-Zurita J. L., Gil-Herrero S., Vincent-Vela M. C., Mendoza-Roca J. A., Alvarez-Blanco S., 2023, Deep study on fouling modelling of ultrafiltration membranes used for OMW treatment: Comparison between semi-empirical models, response surface, and artificial neural networks, Food and Bioprocess Technology, Vol. 16, pp. 2126-2146DOI
3 
Elmakki T., Zavahir S., Gulied M., Qiblawey H., Hammadi B., Khraisheh M., Shon H. K., Park H., Han D. S., 2023, Potential application of hybrid reverse electrodialysis (RED)-forward osmosis (FO) system to fertilizer-producing industrial plant for efficient water reuse, Desalination, Vol. 550, pp. 116374DOI
4 
Goi Y. K., Li M., Liang Y. Y., 2025, A comprehensive review on forward osmosis mass transfer and fouling: Mathematical modeling, mechanism, prediction and optimization, Journal of Water Process Engineering, Vol. 72, pp. 107677DOI
5 
Gosmann L., Geitner C., Wieler N., 2022, Data-driven forward osmosis model development using multiple linear regression and artificial neural networks, Computers & Chemical Engineering, Vol. 165, pp. 107933DOI
6 
Jasim H. K., Al-Ridah Z. A., Naje A. S., 2024, Graphene oxide–carbon nanotube composite membrane for enhanced removal of organic pollutants by forward osmosis, Desalination and Water Treatment, Vol. 318, pp. 100363DOI
7 
Kovacs D. J., Li Z., Baetz B. W., Hong Y., Donnaz S., Zhao X., Zhou P., Ding H., Dong Q., 2022, Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study, Journal of Membrane Science, Vol. 660, pp. 120817DOI
8 
Lee S., Kim Y. C., 2018, Performance analysis of plate-and-frame forward osmosis membrane elements and implications for scale-up design, Journal of Membrane Science, Vol. 550, pp. 219-229DOI
9 
Lee S., 2020, Exploring the operation factors that influence performance of a spiral-wound forward osmosis membrane process for scale-up design, Membranes, Vol. 10, No. 3, pp. 53DOI
10 
Ma S., Wu X., Fan L., Wang Q., Hu Y., Xie Z., 2023, Effect of different draw solutions on concentration polarization in a forward osmosis process: Theoretical modeling and experimental validation, Industrial & Engineering Chemistry Research, Vol. 62, pp. 3672-3683DOI
11 
Mendoza E., Blandin G., Castaño-Trias M., Alonso L. L., Comas J., Buttiglieri G., 2023, Rejection of organic micropollutants from greywater with forward osmosis: A matter of time, Journal of Environmental Chemical Engineering, Vol. 11, pp. 110931DOI
12 
Nurhayati M., You Y., Park J., Lee B. J., Kang H. G., Lee S., 2023, Artificial neural network implementation for dissolved organic carbon quantification using fluorescence intensity as a predictor in wastewater treatment plants, Chemosphere, Vol. 335, pp. 139032DOI
13 
Rachmatullah M. I. C., Santoso J., Surendro K., 2021, Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction, PeerJ Computer Science, Vol. 7, pp. e724DOI
14 
Sheela K. G., Deepa S. N., 2013, Review on methods to fix number of hidden neurons in neural networks, Mathematical Problems in Engineering, Vol. 2013, pp. 425740DOI
15 
Shetty G. R., Chellam S., 2003, Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks, Journal of Membrane Science, Vol. 217, pp. 69-86DOI
16 
Shi F., Lu S., Gu J., Lin J., Zhao C., You X., Lin X., 2022, Modeling and evaluation of the permeate flux in forward osmosis process with machine learning, Industrial & Engineering Chemistry Research, Vol. 61, pp. 18045-18056DOI
17 
Song Q., Jiang H., Liu J., 2017, Feature selection based on FDA and F-score for multi-class classification, Expert Systems with Applications, Vol. 81, pp. 22-27DOI
18 
Van der Bruggen B., 2018, Microfiltration, ultrafiltration, nanofiltration, reverse osmosis, and forward osmosis, Fundamental modelling of membrane systems, Fundamental Modelling of Membrane Systems, pp. 25-70DOI
19 
Wang H., Yang J., Zhang H., Zhao J., Liu H., Wang J., Li G., Liang H., 2023, Membrane-based technology in water and resources recovery from the perspective of water social circulation: A review, The Science of the Total Environment, pp. 168277DOI
20 
Wang L., Li Z., Fan J., Han Z., 2024, The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models, Chemosphere, Vol. 349, pp. 141031DOI
21 
Wang M., Ji Z., Dong Y., 2025, Machine learning-guided performance prediction of forward osmosis polymeric membranes for boron recovery, Water Research, Vol. 281, pp. 123700DOI
22 
Zavahir S., Elmakki T., Gulied M., Shon H. K., Park H., Kakosimos K. E., Han D. S., 2023, Integrated photoelectrochemical (PEC)-forward osmosis (FO) system for hydrogen production and fertigation application, Journal of Environmental Chemical Engineering, Vol. 11, pp. 110525DOI
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