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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

Introduction

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,...

Notes

References

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Copyright information

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

Authors and Affiliations

  1. 1.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

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