1. A.C. Lai, D. Yu, J.H. Lee, Mixing of a rosette jet group in a crossflow, J. Hydraul. Eng., 137 (2011) 787–803.
  2. O. Abessi, P.J. Roberts, V. Gandhi, Rosette diffusers for dense effluents, J. Hydraul. Eng., 143 (2016) 06016029.
  3. O. Abessi, P.J. Roberts, Rosette diffusers for dense effluents inflowing currents, J. Hydraul. Eng., 144 (2017) 06017024.
  4. G.C. Christodoulou, I.G. Papakonstantis, I.K. Nikiforakis, Desalination brine disposal by means of negatively buoyant jets, Desal. Water Treat., 53 (2015) 3208–3213.
  5. N. Ahmad, T. Suzuki, Study of dilution, height, and lateral spread of vertical dense jets in marine shallow water, Water Sci. Technol., 73 (2016) 2986–2997.
  6. J.O.G. Pecly, Estimation of the dilution field near a marine outfall by using effluent turbidity as an environmental tracer and comparison with dye tracer data, Water Sci. Technol., 77 (2018) 269–277.
  7. S.J. Kwon, I.W. Seo, Experimental investigation of wastewater discharges from a Rosette-type riser using PIV, KSCE J. Civ. Eng., 9 (2005) 355–362.
  8. X. Tian, P.J. Roberts, Experiments on marine wastewater diffusers with multiport rosettes, J. Hydraul. Eng., 137 (2011) 1148–1159.
  9. A. Dashti, M. Asghari, H. Solymani, M. Rezakazemi, A. Akbari, Modeling of CaCl2 removal by positively charged polysulfonebased nanofiltration membrane using artificial neural network and genetic programming, Desal. Water Treat., 111 (2018) 57–67.
  10. A.A. Tashvigh, B. Nasernejad, Soft computing method for modeling and optimization of air and water gap membrane distillation–a genetic programming approach, Desal. Water Treat., 76 (2017) 30–39.
  11. R. Hashim, C. Roy, S. Shamshirband, S. Motamedi, A. Fitri, D. Petković, K.I. Song, Estimation of wind-driven coastal waves near a Mangrove forest using adaptive neuro-fuzzy inference system, Water Resour. Manage., 30 (2016) 2391–2404.
  12. Y. Peng, X. Zhang, W. Xu, Y. Shi, Z. Zhang, An optimal algorithm for cascaded reservoir operation by combining the grey forecasting model with DDDP, Water Sci. Technol. Water Supply, 18 (2018) 142–150.
  13. A. Picos, J.M. Peralta-Hernández, Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor, Water Sci. Technol., 78 (2018) 925–935.
  14. X. Xia, S. Jiang, N. Zhou, X. Li, L. Wang, Genetic algorithm hyper-parameter optimization using Taguchi design for groundwater pollution source identification, Water Sci. Technol. Water Supply, 19 (2019) 137–146.
  15. A.A. Tashvigh, F.Z. Ashtiani, M. Karimi, A. Okhovat, A novel approach for estimation of solvent activity in polymer solutions using genetic programming, Calphad, 51 (2015) 35–41.
  16. A.A. Tashvigh, F.Z. Ashtiani, A. Fouladitajar, Genetic programming for modeling and optimization of gas sparging assisted microfiltration of an oil-in-water emulsion, Desal. Water Treat., 57 (2016) 19160–19170.
  17. D.P. Searson, A.H. Gandomi, Handbook of Genetic Programming Applications, Springer, Cham, 2015, pp. 551–573.
  18. M.J.S. Safari, A.D. Mehr, Multi-gene genetic programming for sediment transport modeling in sewers for conditions of nondeposition with a bed deposit, Int. J. Sediment Res., 33 (2018) 262–270.
  19. D.S. Pandey, I. Pan, S. Das, J.J. Leahy, W. Kwapinski, Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier, Bioresour. Technol., 179 (2015) 524–533.
  20. X. Yan, A. Mohammadian, Numerical modeling of vertical buoyant jets subjected to lateral confinement, J. Hydraul. Eng., 143 (2017) 04017016.
  21. X. Yan, A. Mohammadian, Numerical modeling of multiple inclined dense jets discharged from moderately spaced ports, Water, 11 (2019) 1–15.
  22. X. Yan, A. Mohammadian, X. Chen, Three-dimensional numerical simulations of buoyant jets discharged from a rosette-type multiport diffuser, J. Mar. Sci. Eng., 7 (2019) 409.
  23. X. Yan, A. Mohammadian, Multigene genetic-programmingbased models for initial dilution of laterally confined vertical buoyant jets, J. Mar. Sci. Eng., 7 (2019) 246.
  24. X. Yan, A. Mohammadian, Evolutionary modeling of inclined dense jets discharged from multiport diffusers, J. Coastal Res., 36 (2019) 362–371.
  25. X. Yan, A. Mohammadian, Evolutionary prediction of multiple vertical buoyant jets in stationary ambient water, Desal. Water Treat., 178 (2020) 41–52.
  26. S. Zhang, B. Jiang, A.W.K. Law, B. Zhao, Large-eddy simulations of 45 inclined dense jets, Environ. Fluid Mech., 16 (2016) 101–121.
  27. N. Ahmad, R.E. Baddour, Density effects on dilution and height of vertical fountains, J. Hydraul. Eng., 141 (2015) 04015024.
  28. A. Guven, M. Gunal, Prediction of scour downstream of gradecontrol structures using neural networks, J. Hydraul. Eng., 134 (2008) 1656–1660.
  29. H. Bashiri, E. Sharifi, V.P. Singh, Prediction of local scour depth downstream of sluice gates using harmony search algorithm and artificial neural networks, J. Irrig. Drain. Eng., 144 (2018) 06018002.
  30. C.J. Willmott, K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30 (2005) 79–82.
  31. M.V. Shcherbakov, A. Brebels, N.L. Shcherbakova, A.P. Tyukov, T.A. Janovsky, V.A.E. Kamaev, A survey of forecast error measures, World Appl. Sci. J., 24 (2013) 171–176.
  32. G.A.F. Seber, C.J. Wild, Nonlinear Regression, John Wiley & Sons, New York, 1989.
  33. T.P. Lane, W.H. DuMouchel, Simultaneous confidence intervals in multiple regression, Am. Stat., 48 (1994) 315–321.
  34. K.D. Dolan, L. Yang, C.P. Trampel, Nonlinear regression technique to estimate kinetic parameters and confidence intervals in unsteady-state conduction-heated foods, J. Food Eng., 80 (2007) 581–593.