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Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation

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Abstract

The high cost and time for determining water quality parameters justify the importance of application of mathematical models in discovering connection among them. This paper presents a data mining technique and its improved version in estimating water quality parameters. For this purpose, the surface and ground water quality data from Hamedan (Iran) between 2006 and 2015 were analyzed using M5 model tree and its modified version optimized with Excel Solver Platform (ESP). The values of electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), and total hardness (TH) were considered as target variables, whereas pH, concentrations of sodium (Na), chlorine (Cl), bicarbonate (HCO3), sulfate (SO4), magnesium (Mg), calcium (Ca), and potassium (K) were as inputs. The results showed that in both the sources, pH was the least influential parameter on EC, TDS, SAR, and TH. It was found that among the objective parameters, the accuracy of models in estimating TH was higher than the other parameters, whereas SAR was a complex variable. The comparison of performances of the M5 and the M5-ESP models illustrated that the application of the ESP significantly decreased the normal root mean error (NRMSE) of the M5 model; the mean NRMSEs were decreased by 18.95% and 20.29% in estimating groundwater and surface water quality parameters, respectively. Moreover, ability of both the M5 and the M5-ESP models in computing objective parameters of the groundwater was found to be better than the surface water.

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Data availability

Data were obtained from the Ministry of Energy, Regional Water Company of Hamedan, Iran.

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Acknowledgments

The authors appreciate the data source provider. In addition, we appreciate the respected editors and reviewers for their constructive comments.

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Authors and Affiliations

  1. Department of Soil Science, Malayer University, Malayer, Iran

    Maryam Bayatvarkeshi & Mahtab Zarei

  2. Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Monzur Alam Imteaz

  3. Civil Engineering Department, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  4. Institute of Research and Development , Duy Tan University , 550000, Da Nang, Vietnam

    Ozgur Kisi

  5. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Zaher Mundher Yaseen

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  1. Maryam Bayatvarkeshi
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  2. Monzur Alam Imteaz
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  3. Ozgur Kisi
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  4. Mahtab Zarei
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  5. Zaher Mundher Yaseen
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Contributions

Formal analysis and conceptualization: Maryam Bayatvarkeshi and Zaher Mundher Yaseen; methodology: Maryam Bayatvarkeshi and Mahtab Zarei; writing—original draft: Maryam Bayatvarkeshi, Zaher Mundher Yaseen, and Monzur Imteaz; project administrative: Zaher Mundher Yaseen; manuscript revision: Zaher Mundher Yaseen and Ozgur Kisi; supervision: Ozgur Kisi and Zaher Mundher Yaseen.

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Correspondence to Zaher Mundher Yaseen.

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Bayatvarkeshi, M., Imteaz, M., Kisi, O. et al. Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation. Environ Sci Pollut Res 28, 7347–7364 (2021). https://doi.org/10.1007/s11356-020-11047-w

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