References

  1. A.M. Aboukarima, M.A. Al-Sulaiman, M.S.A. EI Marazky, Effect of sodium adsorption ratio and electric conductivity of the applied water on infiltration in a sandy-loam soil, Water SA, 44 (2018) 105–110.
  2. I. Shainberg, J. Letey, Response of soils to sodic and saline conditions, Hilgardia, 52 (1984) 1–57.
  3. E.A. El-Morsy, M. Malik, J. Letey, Interactions between water quality and polymer treatment on infiltration rate and clay migration, Soil Technol., 4 (1991) 221–231.
  4. M.R. Emdad, R. Steven, R.J. Smith, H. Fardad, Effect of water quality on soil structure and infiltration under furrow irrigation, Irrig. Sci., 23 (2004) 55–60.
  5. B.B. Patel, R.S. Dave, Studies on the infiltration of salinealkali soils of several parts of Mehsana and Patan districts of North Gujarat, J. Appl. Technol. Environ. Sanitation, 1 (2011) 87–92.
  6. D.Ö. Faruk, A hybrid neural network and ARIMA model for water quality time series prediction, Eng. Appl. Artif. Intell., 23 (2010) 586–594.
  7. A.W. Jayawardena, F. Lai, Time series analysis of water quality data in Pearl River, China, J. Environ. Eng., 115 (1989) 590–607.
  8. H. Sun, M. Koch, Case study: analysis and forecasting of salinity in Apalachicola Bay, Florida, using Box-Jenkins ARIMA models, J. Hydraul. Eng., 127 (2001) 718–727.
  9. G. Asadollahfardi, Analysis of surface water quality in Tehran, Water Qual. Res. J., 37 (2002) 489–511.
  10. A. Kurnc K. Yürekli, O. Cevik, Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey, Environ. Modell. Software, 20 (2005) 1195–1200.
  11. G. Asadollahfardi, M. Rahbar, M. Fatemiaghda, Application of time series models to predict water quality of upstream and downstream of the Latian Dam in Iran, Univ. J. Environ. Res. Technol., 2 (2012) 26–36.
  12. S.J. Abudu, P. King, Z. Sheng, Comparison of the performance of statistical models in forecasting monthly total dissolved solids in the Rio Grande, J. Am. Water Resour. Assoc., 48 (2012) 10–23.
  13. M. Ranjbar, M. Khaledian, Using ARIMA time series model in forecasting the trend of changes in qualitative parameters of Sefid-Rud River, Int. Res. J. Appl. Basic Sci., 8 (2014) 346–351.
  14. F.K. Arya, L. Zhang, Time series analysis of water quality parameters at Stillaguamish River using order series method, Stochastic Environ. Res. Risk Assess., 29 (2015) 227–239.
  15. M.H. Salmani, E. Salmani Jajaei, Forecasting models for flow and total dissolved solids in Karoun river-Iran, J. Hydrol., 535 (2016) 148–159.
  16. G. Asadollahfardi, A. Hemati, S. Moradinejad, R. Asadollahfardi, Sodium adsorption ratio (SAR) prediction of the Chalghazi river using artificial neural network (ANN) Iran, Curr. World Environ., 8 (2013) 169–178.
  17. A. Azad, H. Karami, S. Farzin, A. Saeedian, H. Kashi, F. Sayyahi, Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (case study: Gorganrood River), KSCE J. Civ. Eng., 22 (2018) 2206–2213.
  18. M.T. Sattari, A. Farkhondeh, J. Patrick Abraham, Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran, Environ. Sci. Pollut. Res., 25 (2018) 4776–4786.
  19. B. Singh, Prediction of the sodium absorption ratio using datadriven models: a case study in Iran, Geol. Ecol. Landscapes, 4 (2020) 1–10.
  20. B.H.K. Al-Obaidi, B.H. Khudhair, R.S. Mahmood, R.A. Kadhim, Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network, J. Eng. Sci. Technol., 15 (2000) 3055–3066.
  21. A. Aslanargun, M. Mammadov, B. Yazici, S. Yolacan, Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting, J. Stat. Comput. Simul., 77 (2007) 29–53.
  22. L.A. Dı´az-Robles, J.C. Ortega, J.S. Fu, G.D. Reed, J.C. Chow, J.G. Watson, J.A. Moncada-Herrera, A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile, Atmos. Environ., 42 (2008) 8331–8340.
  23. A.A. Yassen, Comparative Study of Artificial Neural Network and ARIMA Models for Economic Forecasting, Mater Thesis, Al-Azhar University, Gaza, 2011.
  24. A.A. Adebiyi, A.O. Adewumi, C.K. Ayo, Comparison of ARIMA and artificial neural networks models for stock price prediction, J. Appl. Math., 1 (2014) 1–7.
  25. D.E. Ighravwea, C.O. Anyaeche, A comparison of ARIMA and ANN techniques in predicting port productivity and berth effectiveness, Int. J. Data Network Sci., 3 (2019) 13–22.
  26. Z. Li, Y. Li, A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS, BMC Med. Inf. Decis. Making, 143 (2020) 1–13.
  27. G. Asadollahfardi, H. Zangooi, M. Asadi, M. Tayebi Jebeli, A. Meshkat-Dini, N. Roohani. Comparison of Box–Jenkins time series and ANN in predicting total dissolved solid at the Zāyandé-Rūd River, Iran, J. Water Supply Res. Technol. AQUA, 67(2018) 673–684.
  28. G. Asadollahfardi, N. Heidarzadeh, A. Sekhavati, M. Asadi, Optimization of water quality monitoring stations using dynamic programming approach, a case study of the Mond Basin Rivers, Iran, Environ. Dev. Sustainability, 23 (2021) 2867–2881, doi: 10.1007/s10668–020–00693–2.
  29. E. Rahnama, O. Bazrafshan, G. Asadollahfardi, Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climate in Iran, Arabian J. Geosci., 13 (2020), doi: 10.1007/s12517–020–06146–4.
  30. G.E.P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, 5th ed., Holden Day, San, Francisco, 1976.
  31. W. Wu, G.C. Dandy, H.R. Maier, Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling, Environ. Modell. Software, 54 (2014) 108–127.
  32. H.R. Maier, A. Jain, G.C. Dandy, K.P. Sudheer, Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions, Environ. Modell. Software, 25 (2010) 891–909.
  33. M. Cakmakci, Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge, Bioprocess Biosyst. Eng., 30 (2007) 349–357.
  34. H. Abu Qdais, K. Bani Hani, N. Shatnawi, Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm, Resour. Conserv. Recyl., 54 (2010) 359–363.
  35. G. Asadollahfardi, A. Taklify, A. Ghanbari, Application of artificial neural network to predict TDS in Talkheh Rud River, J. Irrig. Drain. Eng., 138 (2012) 363–370.
  36. T. Beltramo, C. Ranzan, J. Hinrichs, B. Hitzmann, Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm, Biosyst. Eng., 143 (2016) 68–78.
  37. B. Najafi, S. Faizollahzadeh Ardabili, Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC), Resour. Conserv. Recycl., 133 (2018) 169–178.
  38. C.W. Dawson, R.L. Wilby, Hydrological modelling using artificial neural networks, Prog. Phys. Geogr., 25 (2001) 80–108.
  39. T. Kohonen, Self-Organization and Associative Memory, Springer, New York, NY, Berlin, Heidelberg, 1984.
  40. X.M. Song, Radial Basis Function Networks for Empirical Modeling of Chemical Process, MSc Thesis, University of Helsinki, 1996.
  41. P.L. Narasimha, W.H. Delashmit, M.T. Manry, J. Li, F. Maldonado, An integrated growing-pruning method for feedforward network training, Neurocomputing, 71 (2008) 2831–2847.
  42. S. Chen, S.A. Billings, W. Luo, Orthogonal least squares methods and their application to nonlinear system identification, Int. J. Control, 50 (1989) 1873–1896.
  43. S. Chen, C.F.N. Cowan, P.M. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Networks, 2 (1991) 302–309.
  44. 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.
  45. P. Krause, D.P. Boyle, F. Bäse, Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5 (2005) 89–97.
  46. J.E. Nash, J.V. Sutcliffe, River flow forecasting through conceptual models’ part I—a discussion of principles, J. Hydrol., 10 (1970) 282–290.