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Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models

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Abstract

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.

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Acknowledgements

The authors thank Konstantina Pyrgaki for providing the experimental dataset. Also, the authors appreciate the editors and reviewers for their constructive comments on enhancing the manuscript visualization.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Suraj Kumar Bhagat, Tiyasha Tiyasha & Tran Minh Tung

  2. Department of Automotive Engineering, Arba Minch University, Arba Minch, Ethiopia

    Mariapparaj Paramasivan

  3. Civil Engineering Department, University of Technology, Baghdad, Iraq

    Mustafa Al-Mukhtar

  4. Department of Geology & Geoenvironment, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15784, Athens, Greece

    Konstantina Pyrgaki

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

    Zaher Mundher Yaseen

Authors
  1. Suraj Kumar Bhagat
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  2. Mariapparaj Paramasivan
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  3. Mustafa Al-Mukhtar
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  4. Tiyasha Tiyasha
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  5. Konstantina Pyrgaki
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  6. Tran Minh Tung
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  7. Zaher Mundher Yaseen
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Contributions

Suraj Kumar Bhagat: Data curation, formal analysis, methodology, investigation, visualization, writing - original draft, - review and editing draft preparation, resources, software. Mariapparaj Paramasivan: Visualization, writing - original draft, - review and editing draft preparation. Mustafa Al-Mukhtar: Formal analysis, project administration; writing - review and editing. Tiyasha Tiyasha: Data curation, formal analysis, methodology, investigation, visualization, writing - original draft, - review and editing draft preparation. Konstantina Pyrgaki: Writing - original draft, - review and editing draft preparation. Tran Minh Tung: Supervision, conceptualization, project administration, writing - review and editing. Zaher Mundher Yaseen: Supervision, conceptualization, project administration, writing - review and editing.

Corresponding author

Correspondence to Zaher Mundher Yaseen.

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The authors declare no competing interests.

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Bhagat, S.K., Paramasivan, M., Al-Mukhtar, M. et al. Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models. Environ Sci Pollut Res 28, 31670–31688 (2021). https://doi.org/10.1007/s11356-021-12836-7

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  • DOI: https://doi.org/10.1007/s11356-021-12836-7

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