1. W.A. Jury, H.J. Vaux Jr., The emerging global water crisis: managing scarcity and conflict between water users, Adv. Agron., 95 (2007) 1–76.
  2. N. AlSawaftah, W. Abuwatfa, N. Darwish, G. Husseini, A comprehensive review on membrane fouling: mathematical modelling, prediction, diagnosis, and mitigation, Water, 13 (2021) 1327, doi: 10.3390/w13091327.
  3. H.F. Ridgway, A. Kelly, C. Justice, B.H. Olson, Microbial fouling of reverse-osmosis membranes used in advanced wastewater treatment technology: chemical, bacteriological, and ultrastructural analyses, Appl. Environ. Microbiol., 45 (1983) 1066–1084.
  4. G. Belfort, R.H. Davis, A.L. Zydney, The behavior of suspensions and macromolecular solutions in crossflow microfiltration, J. Membr. Sci., 96 (1994) 1–58.
  5. M.F.A. Goosen, S.S. Sablani, D. Jackson, Fouling of reverse osmosis and ultrafiltration membranes: a critical review, Sep. Sci. Technol., 39 (2005) 2261–2297.
  6. S. Shirazi, C.-J. Lin, D. Chen, Inorganic fouling of pressuredriven membrane processes — a critical review, Desalination, 250 (2010) 236–248.
  7. Q.-F. Liu, S.-H. Kim, Evaluation of membrane fouling models based on bench-scale experiments: a comparison between constant flowrate blocking laws and artificial neural network (ANNs) model, J. Membr. Sci., 310 (2008) 393–401.
  8. S. Gray, R. Semiat, M.C. Duke, A. Rahardianto, Y. Cohen, Seawater Use and Desalination Technology, In: Treatise on Water Science, Elsevier, 2011, pp. 73–109.
  9. J.-L. Dirion, M. Cabassud, M.V. Le Lann, G. Casamatta, Development of adaptive neural networks for flexible control of batch processes, Chem. Eng. J. Biochem. Eng. J., 63 (1996) 65–77.
  10. W. Richard Bowen, M.G. Jones, J.S. Welfoot, H.N.S. Yousef, Predicting salt rejections at nanofiltration membranes using artificial neural networks, Desalination, 129 (2000) 147–162.
  11. A. Kapoor, S. Balasubramanian, P. Muthamilselvi, V. Vaishampayan, S. Prabhakar, Lab-on-a-Chip Devices for Water Quality Monitoring, Inamuddin, A. Asiri, Eds., Nanosensor Technologies for Environmental Monitoring. Nanotechnology in the Life Sciences, Springer, Cham, 2020.
  12. A. Jang, Z. Zou, K.K. Lee, C.H. Ahn, P.L. Bishop, State-of-the-art lab chip sensors for environmental water monitoring, Meas. Sci. Technol., 22 (2011) 032001, doi: 10.1088/0957-0233/22/3/032001.
  13. X. Pascual, H. Gu, A.R. Bartman, A. Zhu, A. Rahardianto, J. Giralt, R. Rallo, P.D. Christofides, Y. Cohen, Data-driven models of steady state and transient operations of spiral-wound RO plant, Desalination, 316 (2013) 154–161.
  14. A. Abdelrasoul, H. Doan, A. Lohi, A mechanistic model for ultrafiltration membrane fouling by latex, J. Membr. Sci., 433 (2013) 88–99.
  15. N. Peña, S. Gallego, F. del Vigo, S.P. Chesters, Evaluating impact of fouling on reverse osmosis membranes performance, Desal. Water Treat., 51 (2012) 958–968.
  16. B. Gu, X.Y. Xu, C.S. Adjiman, A predictive model for spiral wound reverse osmosis membrane modules: the effect of winding geometry and accurate geometric details, Comput. Chem. Eng., 96 (2017) 248–265.
  17. R. Rivas-Perez, J. Sotomayor-Moriano, G. Pérez-Zuñiga, M.E. Soto-Angles, Real-time implementation of an expert model predictive controller in a pilot-scale reverse osmosis plant for brackish and seawater desalination, Appl. Sci., 9 (2019) 2932, doi: 10.3390/app9142932.
  18. U. Ahmed, R. Mumtaz, H. Anwar, A.A. Shah, R. Irfan, J. García- Nieto, Efficient water quality prediction using supervised machine learning, Water, 11 (2019) 2210, doi: 10.3390/w11112210.
  19. G. Hayder, I. Kurniawan, H.M. Mustafa, Implementation of machine learning methods for monitoring and predicting water quality parameters, Biointerface Res. Appl. Chem., 11 (2021) 9285–9295.
  20. A.H. Haghiabi, A.H. Nasrolahi, A. Parsaie, Water quality prediction using machine learning methods, Water Qual. Res. J., 53 (2018) 3–13.
  21. N. AlSawaftah, W. Abuwatfa, N. Darwish, G. Husseini, A comprehensive review on membrane fouling: mathematical modelling, prediction, diagnosis, and mitigation, Water, 13 (2021) 1327, doi: 10.3390/w13091327.
  22. A. Kadiwal, Water Quality, Kaggle, 25 Apr. 2021, Available at
  23. Y. Zhang, Support Vector Machine Classification Algorithm and Its Application, C. Liu, L. Wang, A. Yang, Eds., Information Computing and Applications, ICICA 2012, Communications in Computer and Information Science, Vol. 308, Springer, Berlin, Heidelberg, 2012, pp. 179–186. doi: 10.1007/978-3-642-34041-3_27
  24. K. Taunk, S. De, S. Verma, A. Swetapadma, A Brief Review of Nearest Neighbor Algorithm for Learning and Classification, 2019 International Conference on Intelligent Computing and Control Systems (ICCS), IEEE, Madurai, India, 2019, pp. 1255–1260. doi: 10.1109/ ICCS45141.2019.9065747
  25. P.O. Gislason, J.A. Benediktsson, J.R. Sveinsson, Random Forest Classification of Multisource Remote Sensing and Geographic Data, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, AK, USA, 2004, pp. 1049–1052. doi: 10.1109/ IGARSS.2004.1368591
  26. K.P. Singh, A. Basant, A. Malik, G. Jain, Artificial neural network modeling of the river water quality—a case study, Ecol. Modell., 220 (2009) 888–895.
  27. J. Davis, M. Goadrich, The Relationship Between Precision- Recall and ROC Curves, Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, 2006, pp. 233–240.
    doi: 10.1145/1143844. 1143874
  28. J.N. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment, J. Thoracic Oncol., 5 (2010) 1315–1316.