1. M. Salari, E.S. Salami, S.H. Afzali, M. Ehteshami, G.O. Conti, Z. Derakhshan, S.N. Sheibani, Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water, Food Chem. Technol., 118 (2018) 212–219.
  2. E.S. Salami, M. Ehteshami, Application of artificial neural networks to estimating DO and salinity in San Joaquin River basin, Desal. Water Treat., 57 (2016) 4888–4897.
  3. E.S. Salami, M. Salari, M. Ehteshami, N.T. Bidokhti, H. Ghadimi, Application of artificial neural networks and mathematical modeling for the prediction of water quality variables (case study: southwest of Iran), Desal. Water Treat., 57 (2016) 27073–27084.
  4. P.G. Whitehead, Water Quality Modeling, Wiley StatsRef: Statistics Reference Online: John Wiley & Sons, 2016, pp. 1–22.
  5. M. Yuceer, M.A. Coskun, Modeling water quality in rivers: a case study of Beylerderesi river in Turkey, Appl. Ecol. Environ. Res., 14 (2016) 383–395.
  6. M. Salari, M. Hosseinikheirabad, M. Ehteshami, S.N. Moaddeli, E. Teymouri, Modeling of groundwater quality for drinking and agricultural purpose: a case study in kahorestan plain, J. Environ. Treat. Tech., 8 (2020) 346–352.
  7. Q. Wang, S. Li, P. Jia, C. Qi, F. Ding, A review of surface water quality models, Sci. World J., 2013 (2013) 1–7.
  8. N. Rahmanian, S.H.B. Ali, M. Homayoonfard, N.J. Ali, M. Rehan, Y. Sadef, A.S. Nizami, Analysis of physiochemical parameters to evaluate the drinking water quality in the state of Perak, Malaysia, J. Chem., 2015 (2015) 1–10.
  9. A. Moayedi, B. Yargholi, E. Pazira, H. Babazadeh, Investigated of desalination of saline waters by using dunaliella salina algae and its effect on water ions, Civ. Eng. J., 5 (2019) 2450–2460.
  10. M. Pal, N.R. Samal, P.K. Roy, M.B. Roy, Electrical conductivity of lake water as environmental monitoring – a case study of Rudrasagar Lake, IOSR J. Environ. Sci. Toxicol. Food Technol., 9 (2015) 66–71.
  11. H.Y. Aldosky, S.M.H. Shamdeen, A new system for measuring electrical conductivity of water as a function of admittance, J. Electr. Bioimpedance, 2 (2011) 86–92.
  12. B. Tutmez, Z. Hatipoglu, U. Kaymak, Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system, Comput. Geosci., 32 (2006) 421–433.
  13. M.A.S. Polash, M.A.S. Akil, M.T.U. Arif, M.A. Hossain, Effect of salinity on osmolytes and relative water content of selected rice genotypes, Trop. Plant Res., 5 (2018) 227–232.
  14. M.A. Nahian, A. Ahmed, A.N. Lázár, C.W. Hutton, M. Salehin, P.K. Streatfield, Drinking water salinity associated health crisis in coastal Bangladesh, Elem. Sci. Anthropocene, 6 (2018) 1–14.
  15. M.Z. Alam, L.C. Boggs, S. Mitra, M.M. Haque, J. Halsey, M. Rokonuzzaman, B. Saha, M. Moniruzzaman, Effect of salinity intrusion on food crops, livestock and fish species at Kalapara coastal belt in Bangladesh, J. Food Qual., 2017 (2017) 1–23.
  16. S. Yousfi, M.D. Serret, J. Voltas, J.L. Araus, Effect of salinity and water stress during the reproductive stage on growth, ion concentrations, D13C, and D15N of durum wheat and related amphiploids, J. Exp. Bot., 61 (2010) 3529–3542.
  17. N. Qin, Y. Wu, H.W. Wang, Y.Y. Wang, Experimental study and numerical simulation of the salinity effect on water-freezing point and ice-melting rate, IOP Conf. Ser.: Mater. Sci. Eng., 283 (2017) 1–8.
  18. T.H. Kim, J.H. Kang, S.H. Kim, I.S. Choi, K.H. Chang, J.M. Oh, K.H. Kim, Impact of salinity change on water quality variables from the sediment of an artificial lake under anaerobic conditions, Sustainability, 9 (2017) 1–8.
  19. S. Thirumalini, K. Joseph, Correlation between electrical conductivity and total dissolved solids in natural waters, Malaysian J. Sci., 28 (2009) 55–61.
  20. A.F. Rusydi, Correlation between conductivity and total dissolved solid in various type of water: a review, Earth Environ. Sci., 118 (2018) 1–6.
  21. L.N. Nthunya, S. Maifadi, B.B. Mamba, A.R. Verliefde, S.D. Mhlanga, Spectroscopic determination of water salinity in brackish surface water in Nandoni dam, at Vhembe district, Limpopo province, South Africa, Water, 990 (2018) 1–13.
  22. M.C. McCutcheon, H.J. Farahani, J.D. Stednick, G.W. Buchleiter, T.R. Green, Effect of soil water on apparent soil electrical conductivity and texture relationships in a dry land field, Biosyst. Eng., 94 (2006) 19–32.
  23. B.S.R.V. Prasad, P.D.N. Srinivasu, P.S. Varma, A.V. Raman, S. Ray, Dynamics of dissolved oxygen in relation to saturation and health of an aquatic body: a case for Chilka lagoon, India, J. Ecosyst., 2014 (2014) 1–17.
  24. V.S. Kale, Consequence of temperature, pH, turbidity and dissolved oxygen water quality parameters, Int. Adv. Res. J. Sci. Eng. Technol., 3 (2016) 186–190.
  25. A.S. Ren, F. Chai, H. Xue, D.M. Anderson, F.P. Chavez, A sixteen year decline in dissolved oxygen in the central California current, Sci. Rep., 8 (2018) 1–9.
  26. T. Näykki, L. Jalukse, I. Helm, I. Leito, Dissolved oxygen concentration interlaboratory comparison: what can we learn?, Water, 5 (2013) 420–442.
  27. E.S. Salami, M. Salari, S. Nikbakht Sheibani, M. Hosseinikheirabad, Dataset on the assessments the rate of changing of dissolved oxygen and temperature of surface water, case study: California, USA, J. Environ. Treat. Tech., 7 (2020) 843–852.
  28. S. Famielec, M. Malinowski, B. Brzychczyk, J. Salamon, Present used methods for measuring dissolved oxygen concentration at wastewater treatment plants, Infrastruct. Ecol. Rural Area, 2 (2015) 431–440.
  29. Y. Chen, J. Xu, H. Yu, Z. Zhen, D. Li, Three-dimensional shortterm prediction model of dissolved oxygen content based on PSO-BPANN algorithm coupled with Kriging interpolation, Math. Prob. Eng., 2016 (2016) 1–10.
  30. V. Ranković, J. Radulović, I. Radojević, A. Ostojić, L. Čomić, Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system, J. Hydroinf., 14 (2012) 167–179.
  31. M. Ay, Ö. Kisi, Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques, J. Civ. Eng., 21 (2017) 1631–1639.
  32. X. Li, J. Sha, Z.L. Wang, Chlorophyll-a prediction of lakes with different water quality patterns in China based on hybrid neural networks, Water, 524 (2017) 1–13.
  33. M. Devercelli, E. Peruchet, Trends in chlorophyll-a concentration in urban water bodies within different man-used basins, Int. J. Limnol., 44 (2007) 75–84.
  34. J. Pitarch, G. Volpe, S. Colella, H. Krasemann, R. Santoleri, Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data, Ocean Sci., 12 (2016) 379–389.
  35. S. Jamshidi, N.B. Abu Bakar, A study on distribution of chlorophyll-a in the coastal waters of Anzali Port, south Caspian Sea, Ocean Sci. Dis., 8 (2011) 435–451.
  36. J.B. Palter, M.S. Lozier, R.T. Barber, The effect of advection on the nutrient reservoir in the North Atlantic subtropical gyre, Nature, 437 (2005) 687–692.
  37. C.E. Fergus, A.O. Finley, P.A. Soranno, T. Wagner, Spatial variation in nutrient and water color effects on lake chlorophyll at Macroscales, PLoS One, 11 (2016) 1–20.
  38. G.F. Lee, R.A. Jones, Chlorophyll-a raw water quality parameter, J. Am. Water Works Assoc., 74 (1982) 490–494.
  39. A. Wirasatriya, A. Kunarso, L. Maslukah, A. Satriadi, R.D. Armanto, Different responses of chlorophyll-a concentration and sea surface temperature (SST) on southeasterly wind blowing in the Sunda Strait, IOP Conf. Ser.: Earth Environ. Sci., 139 (2018) 1–7.
  40. K.T. Chandramohanan, V.V. Radhakrishnan, E.A. Joseph, K.V. Mohanan, A study on the effect of salinity stress on the chlorophyll content of certain rice cultivars of Kerala state of India, Agric. For. Fish., 3 (2014) 67–70.
  41. Standard Methods for the Examination of Water and Wastewater, 19th ed., American Public Health Association, Washington, DC, 1996, p. 541.
  42. M.B. Menhaj, Fundamental of Neural Network, Vol. 1. Industrial Amir Kabir University, Tehran, 2008.
  43. E.G. Farmaki, N.S. Thomaidis, C.E. Efstathiou, Artificial neural networks in water analysis: theory and applications, Int. J. Environ. Anal. Chem., 90 (2010) 85–105.
  44. M.G. Moghaddam, F.B.H. Ahmad, M. Basri, M.B. Abdul Rahman, Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester, Electron. J. Biotechnol., 13 (2010) 1–12.
  45. A.A. Adebiyi, A.O. Adewumi, C.K. Ayo, Comparison of ARIMA and artificial neural networks models for stock price prediction, J. Appl. Math., 2014 (2014) 1–7.
  46. A.S. Dawood, H.K. Hussain, A. Hassan, Modeling of river water quality parameters using artificial neural network – a case study, Int. J. Adv. Mech. Civ. Eng., 3 (2016) 51–55.
  47. H. Vicente, C. Couto, J. Machado, A. Abelha, J. Neves, Prediction of water quality parameters in a reservoir using artificial neural networks, Int. J. Des. Nat. Ecodyn., 7 (2012) 310–319.
  48. B. Shrestha, P. Nader-Tehrani, Chapter 6: Using DSM2 to Develop Operation Strategies for South Delta Improvements Program’s Proposed Permanent Gates, Submitted for Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh, 27th Annual Progress Report, 2006.
  49. G.A.C. Cordoba, L. Tuhovčák, M. Tauš, Using artificial neural network models to assess water quality in water distribution networks, Proc. Eng., 70 (2014) 399–408.
  50. J. Wang, P. Shi, P. Jiang, J. Hu, S. Qu, X. Chen, Y. Chen, Y. Dai, Z. Xiao, Application of BP neural network algorithm in traditional hydrological model for flood forecasting, Water, 48 (2017) 1–16.
  51. A. Kiraz, O. Canpolat, E.F. Erkan, Ç. Özer, Artificial neural networks modeling for the prediction of Pb(II) adsorption, Int. J. Environ. Sci. Technol., 16 (2019) 5079–5086.
  52. D.S. Levine, Neural network modeling of emotion, Phys. Life Rev., 4 (2007) 37–63.
  53. M.T. Hhagan, H.B. Demuth, M.H. Beale, O. De Jesus, Neural Network Design. Available at: NNDesign.pdf
  54. P. Liu, J. Wang, A.K. Sangaiah, Y. Xie, X. Yin, Analysis and prediction of water quality using LSTM deep neural networks in IOT environment, Sustainability, 11 (2019) 1–19.
  55. S. Kalantary, A. Jahani, R. Pourbabaki, Z. Beigzadeh, Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies, RSC Adv., 9 (2019) 24858–24874.
  56. G. Rajiv, A.N. Singh, S. Anupam, Application of ANN for water quality index, Int. J. Mach. Learn. Comput., 9 (2019) 1–15.
  57. E.S. Salami, M. Ehetshami, A. Karimi-Jashni, M. Salari, S. Nikbakht Sheibani, A. Ehteshami, A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance, Model. Earth Syst. Environ., 2 (2016) 1–11.
  58. M. Salari, E.S. Salami, M. Ehteshami, S. Nikbakht Sheibani, Artificial neural network (ANN) modeling of cavitation mechanism by ultrasonic irradiation for cyanobacteria growth inhibition, J. Environ. Treat. Tech., 8 (2020) 625–633.