A Gamma Test Driven Framework for Predicting Fluoride Adsorption Capacity of Biochar Using Multiple Artificial Intelligence Models

Authors

  • Himanshu Adhikari Department of Farm Machinery and Power Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Author
  • Arun Kumar Department of Farm Machinery and Power Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India; Author
  • Mohammad Ali Nazari Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinskeho 9, 812 37 Bratislava, Slovakia Author
  • Rajat Kumar Sharma Pyrolysis Research Facility, Ground Up Eco Waste Private Limited, Maharashtra, India Author
  • Dinesh Kumar Vishwakarma Department of Civil Engineering, Graphic Era deemed to be University, Dehradun, Uttarakhand, India Author
  • Akarsh Verma Department of Mechanical Science and Bioengineering, Osaka University, Osaka 560-8531, Japan Author

DOI:

https://doi.org/10.52151/jae2026632.2007

Keywords:

data-driven modelling, defluoridation, environmental remediation, machine learning, sorption efficiency, sustainable adsorbents

Abstract

Adsorption of fluoride using biochar signifies a promising and innovative method under current study. The complexity of determining biochar's adsorption properties cannot be fully depicted by traditional mathematical modelling alone. This study presents a novel contribution by applying multiple artificial intelligence techniques to model fluoride adsorption. By considering both proximate and elemental properties of biochar as input variables, this research utilizes the Gamma test (GT) to identify ideal input combinations for the M5 Tree, SVM (Support Vector Machine), MARS (Multivariate Adaptive Regression Splines), MGGP (Multi-Gene Genetic Programming), and MM-ANN (Modular Multilayer Artificial Neural Networks) models. The comparative analysis of these predictive models was evaluated using various statistical metrics (Nash-Sutcliffe efficiency (NSE), Willmott's index of agreement (WI), root mean squared error (RMSE), mean absolute percentage error (MAPE), and Legates and McCabe's index (LM)). The MARS-4 model with four input variables exhibited the highest accuracy with MAPE = 102%, RMSE = 2.56 mg g-1, LM = 0.57, WI = 0.93, and NSE = 0.83 in predicting fluoride adsorption during the testing phase. The MARS-4 model was followed by M5Tree-4 model. These results highlight the MARS-4 model's effectiveness and its potential to offer valuable insights for optimizing biochar for fluoride removal. The study showed enhanced predictive accuracy for fluoride adsorption and guidance for future biochar engineering.

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Published

2026-06-02

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Vol. 63 No. 2 (2026)

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Regular Issue

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Adhikari, H., Kumar, A., Nazari, M. A., Sharma, R. K., Vishwakarma, D. K., & Verma, A. (2026). A Gamma Test Driven Framework for Predicting Fluoride Adsorption Capacity of Biochar Using Multiple Artificial Intelligence Models. Journal of Agricultural Engineering (India), 63(2), 466-479. https://doi.org/10.52151/jae2026632.2007