Environmental Science and Pollution Research

, Volume 26, Issue 10, pp 10439–10440 | Cite as

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 MohammadiEmail author
Letter to the Editor


The abilities of artificial intelligence techniques such as artificial neural networks (ANN) and Support Vector Regression (SVM) today have been well documented in engineering sciences (Buyukyildiz et al. 2014; Fahimi et al. 2017; Kim and Seo 2015; Moazenzadeh et al. 2018; Emamgholizadeh et al. 2018). These methods can perfectly model complex and nonlinear structures, as well as with high processing power and quick computations in engineering sciences (Moazenzadeh et al. 2018; Swenson and Wahr 2006; Holmes et al. 2005; Zhang et al. 2003). Research on these method can be usefully provided that a research (original paper) helps other researchers around the world when finding a research for other researchers that the details of the research and the process of work will be transparent to everyone, especially in the case of highly sensitive artificial intelligence and a small change in the parameters of these models can change the result of the research. In this discussion,...



  1. 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. CrossRefGoogle Scholar
  2. 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. CrossRefGoogle Scholar
  3. 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. CrossRefGoogle Scholar
  4. 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. CrossRefGoogle Scholar
  5. 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. CrossRefGoogle Scholar
  6. 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. CrossRefGoogle Scholar
  7. 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. CrossRefGoogle Scholar
  8. 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. CrossRefGoogle Scholar
  9. Swenson S, Wahr J (2006) Post-processing removal of correlated errors in GRACE data. Geophys Res Lett 33(8).
  10. 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. CrossRefGoogle Scholar
  11. Zhang S, Zhang C, Yang Q (2003) Data preparation for data mining. Appl Artif Intell 17(5–6):375–381. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

Personalised recommendations