1. R. Colciaghi, R. Simonetti, L. Molinaroli, M. Binotti, G. Manzolini, Potentialities of thermal responsive polymer in forward osmosis (FO) process for water desalination, Desalination, 519 (2021) 115311, doi: 10.1016/j.desal.2021.115311.
  2. D.J. Johnson, W.A. Suwaileh, A.W. Mohammed, N. Hilal, Osmotic’s potential: an overview of draw solutes for forward osmosis, Desalination, 434 (2018) 100–120.
  3. N. Ghaffour, T.M. Missimer, G.L. Amy, Technical review and evaluation of the economics of water desalination: current and future challenges for better water supply sustainability, Desalination, 309 (2013) 197–207.
  4. K. Park, J. Kim, D.R. Yang, S. Hong, Towards a low-energy seawater reverse osmosis desalination plant: a review and theoretical analysis for future directions, J. Membr. Sci., 595 (2020) 117607, doi: 10.1016/j.memsci.2019.117607.
  5. K. Park, Y.H. Jang, J.W. Chang, D.R. Yang, Membrane transport behavior characterization method with constant water flux in pressure-assisted forward osmosis, Desalination, 498 (2021) 114738, doi: 10.1016/j.desal.2020.114738.
  6. T.-S. Chung, S. Zhang, K.Y. Wang, J. Su, M.M. Ling, Forward osmosis processes: yesterday, today and tomorrow, Desalination, 287 (2012) 78–81.
  7. M.-k. Kim, J.W. Chang, K. Park, D.R. Yang, Comprehensive assessment of the effects of operating conditions on membrane intrinsic parameters of forward osmosis (FO) based on principal component analysis (PCA), J. Membr. Sci., 641 (2022) 119909, doi: 10.1016/j.memsci.2021.119909.
  8. G.T. Gray, J.R. McCutcheon, M. Elimelech, Internal concentration polarization in forward osmosis: role of membrane orientation, Desalination, 197 (2006) 1–8.
  9. B. Kim, G. Gwak, S. Hong, Analysis of enhancing water flux and reducing reverse solute flux in pressure assisted forward osmosis process, Desalination, 421 (2017) 61–71.
  10. A. Rajkomar, J. Dean, I. Kohane, Machine learning in medicine, New. Eng. J. Med., 380 (2019) 1347–1358.
  11. M.J. Volk, I. Lourentzou, S. Mishra, L.T. Vo, C. Zhai, H. Zhao, Biosystems design by machine learning, ACS Synth. Biol., 9 (2020) 1514–1533.
  12. S. Stocker, G. Csányi, K. Reuter, J.T. Margraf, Machine learning in chemical reaction space, Nat. Commun., 11 (2020) 1–11.
  13. G.N. Marichal Plasencia, J. Camacho-Espino, D. Ávila Prats, B. Peñate Suárez, Machine learning models applied to manage the operation of a simple SWRO desalination plant and its application in marine vessels, Water, 13 (2021) 2547, doi: 10.3390/w13182547.
  14. K. Aghilesh, A. Mungray, S. Agarwal, J. Ali, M.C. Garg, Performance optimisation of forward-osmosis membrane system using machine learning for the treatment of textile industry wastewater, J. Cleaner Prod., 289 (2021) 125690, doi: 10.1016/j.jclepro.2020.125690.
  15. E.A. Roehl Jr., D.A. Ladner, R.C. Daamen, J.B. Cook, J. Safarik, D.W. Phipps Jr., P. Xie, Modeling fouling in a large RO system with artificial neural networks, J. Membr. Sci., 552 (2018) 95–106.
  16. J. Jawad, A.H. Hawari, S. Zaidi, Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux, Desalination, 484 (2020) 114427, doi: 10.1016/j.desal.2020.114427.
  17. Y. Xu, X. Peng, C.Y. Tang, Q.S. Fu, S. Nie, Effect of draw solution concentration and operating conditions on forward osmosis and pressure retarded osmosis performance in a spiral wound module, J. Membr. Sci., 348 (2010) 298–309.
  18. S. Sousa, F.G. Martins, M.C. Alvim-Ferraz, M.C. Pereira, Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environ. Modell. Software, 22 (2007) 97–103.
  19. A.F. Mashaly, A. Alazba, MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment, Comput. Electron. Agric., 122 (2016) 146–155.
  20. F. Khademi, S.M. Jamal, N. Deshpande, S. Londhe, Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression, Int. J. Sustainable Built Environ., 5 (2016) 355–369.
  21. G.K. Uyanık, N. Güler, A study on multiple linear regression analysis, Procedia Soc. Behav. Sci., 106 (2013) 234–240.
  22. W. Dong, Y. Huang, B. Lehane, G. Ma, XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring, Autom. Constr., 114 (2020) 103155, doi: 10.1016/j.autcon.2020.103155.
  23. Y. Liang, J. Wu, W. Wang, Y. Cao, B. Zhong, Z. Chen, Z. Li, Product Marketing Prediction Based on XGBoost and LightGBM Algorithm, Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition, Association for Computing Machinery, New York, NY, United States, 2019, pp. 150–153, doi: 10.1145/3357254.3357290.
  24. X. Ma, J. Sha, D. Wang, Y. Yu, Q. Yang, X. Niu, Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGBoost algorithms according to different high dimensional data cleaning, Electron. Commer. Res. Appl., 31 (2018) 24–39.
  25. J. Brownlee, XGBoost With Python: Gradient boosted Trees with XGBoost and Scikit-Learn, Machine Learning Mastery, 2016.
  26. A. Shehadeh, O. Alshboul, R.E. Al Mamlook, O. Hamedat, Machine learning models for predicting the residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost regression, Autom. Constr., 129 (2021) 103827, doi: 10.1016/j.autcon.2021.103827.
  27. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.-Y. Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017.
  28. M.R. Machado, S. Karray, I.T. de Sousa, LightGBM: An Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry, 2019 14th International Conference on Computer Science & Education (ICCSE), IEEE, Toronto, ON, Canada, 2019, pp. 1111–1116.
  29. H. Gholami, A. Mohamadifar, A. Sorooshian, J.D. Jansen, Machine-learning algorithms for predicting land susceptibility to dust emissions: the case of the Jazmurian Basin, Iran, Atmos. Pollut. Res., 11 (2020) 1303–1315.
  30. F. Arcadu, F. Benmansour, A. Maunz, J. Willis, Z. Haskova, M. Prunotto, Deep learning algorithm predicts diabetic retinopathy progression in individual patients, npj Digit. Med., 2 (2019) 92, doi: 10.1038/s41746-019-0172-3.
  31. H.A. Fayed, A.F. Atiya, Speed up grid-search for parameter selection of support vector machines, Appl. Soft Comput., 80 (2019) 202–210.
  32. K. Park, H. Heo, D.Y. Kim, D.R. Yang, Feasibility study of a forward osmosis/crystallization/reverse osmosis hybrid process with high-temperature operation: modeling, experiments, and energy consumption, J. Membr. Sci., 555 (2018) 206–219.
  33. K. Park, Y.H. Jang, M.-g. Kim, D.R. Yang, S. Hong, Comprehensive analysis of a hybrid FO/crystallization/RO process for improving its economic feasibility to seawater desalination, Water Res., 171 (2020) 115426, doi: 10.1016/j.watres.2019.115426.
  34. W. Suwaileh, N. Pathak, H. Shon, N. Hilal, Forward osmosis membranes and processes: a comprehensive review of research trends and future outlook, Desalination, 485 (2020) 114455, doi: 10.1016/j.desal.2020.114455.
  35. T. Yun, Y.-J. Kim, S. Lee, S. Hong, G.I. Kim, Flux behavior and membrane fouling in pressure-assisted forward osmosis, Desal. Water Treat., 52 (2014) 564–569.
  36. M. Tang, Q. Zhao, S.X. Ding, H. Wu, L. Li, W. Long, B. Huang, An improved LightGBM algorithm for online fault detection of wind turbine gearboxes, Energies, 13 (2020) 807, doi: 10.3390/en13040807.
  37. M. Xie, W.E. Price, LD. Nghiem, M. Elimelech, Effects of feed and draw solution temperature and transmembrane temperature difference on the rejection of trace organic contaminants by forward osmosis, J. Membr. Sci., 438 (2013) 57–64.