Letter to the editor “Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran” by Mohammad Taghi Sattari, Arya Farkhondeh, and John Patrick Abraham

Babak Mohammadi1
1College of Hydrology and Water Resources, Hohai University, Nanjing, China

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Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28:4747–4763. https://doi.org/10.1007/s11269-014-0773-1

Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth, Parts A/B/C 31(18):1164–1171. https://doi.org/10.1016/j.pce.2006.03.020

Emamgholizadeh S, Esmaeilbeiki F, Mohammadi B, zarehaghi D, marofpoor I, Rezaei H (2018) Estimation of the organic carbon content by the pattern recognition method. Commun Soil Sci Plant Anal 49(14):1–12. https://doi.org/10.1080/00103624.2018.1499750

Fahimi F, Yaseen ZM, El-shafie A (2017) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol 128:875–903. https://doi.org/10.1007/s00704-016-1735-8

Holmes C, Drinkwater BW, Wilcox PD (2005) Post-processing of the full matrix of ultrasonic transmit–receive array data for non-destructive evaluation. NDT Int 38(8):701–711. https://doi.org/10.1016/j.ndteint.2005.04.002

Kim SE, Seo IW (2015) Artificial neural network ensemble modeling with conjunctive data clustering for water quality prediction in rivers. J Hydro-environ Res 9(3):325–339. https://doi.org/10.1016/j.jher.2014.09.006

Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597. https://doi.org/10.1080/19942060.2018.1482476

Sattari MT, Farkhondeh A, Abraham JP (2018) Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran. Environ Sci Pollut Res 25(5):4776–4786. https://doi.org/10.1007/s11356-017-0844-y

Swenson S, Wahr J (2006) Post-processing removal of correlated errors in GRACE data. Geophys Res Lett 33(8). https://doi.org/10.1029/2005GL025285

Wu C, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167. https://doi.org/10.1016/j.jhydrol.2010.05.040

Zhang S, Zhang C, Yang Q (2003) Data preparation for data mining. Appl Artif Intell 17(5–6):375–381. https://doi.org/10.1080/713827180