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<p>Accurate forecasting of water quality is pivotal for compelling pollution control and enhanced water management practices. This study predicted total dissolved solids in groundwater samples from West Bengal, India, using data sourced from the Central Pollution Control Board for the span 2020–2022. The parameters include temperature, pH, conductivity, biological oxygen demand, nitrate-N + nitrite-N, fecal coliform, total coliform, fluoride, and arsenic. Employing a diverse set of machine learning models including seven regression models, three support vector machines&#xa0;(SVMs), three artificial neural networks (ANNs), and an adaptive neuro-fuzzy inference system (ANFIS), the study evaluated model performance using root mean square error (RMSE), coefficient of determination (R<sup>2</sup>), mean square error (MSE), and mean absolute error (MAE). The assessment revealed that the ANN trained with Bayesian regularization emerged as the most effective, boasting the lowest errors (RMSE = 0.00147, MSE = 0.0005, MAE = 0.0112) and the highest R<sup>2</sup> (0.97), ensuring superior precision. Additionally, ANN trained with Levenberg–Marquardt and ANFIS exhibit commendable performance, showcasing minimal errors and high R<sup>2</sup> values. Among the non-ANN models, boosted tree displayed a lower RMSE (0.08246) and a higher R<sup>2</sup> (0.62), while a linear SVM demonstrated balanced performance with RMSE of 0.0877 and R<sup>2</sup> of 0.57.</p>

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Estimating Total Dissolved Solids in Groundwater Using Machine Learning Models

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Sumita Gulati,
  • Anshul Bansal,
  • Ashok Pal

摘要

Accurate forecasting of water quality is pivotal for compelling pollution control and enhanced water management practices. This study predicted total dissolved solids in groundwater samples from West Bengal, India, using data sourced from the Central Pollution Control Board for the span 2020–2022. The parameters include temperature, pH, conductivity, biological oxygen demand, nitrate-N + nitrite-N, fecal coliform, total coliform, fluoride, and arsenic. Employing a diverse set of machine learning models including seven regression models, three support vector machines (SVMs), three artificial neural networks (ANNs), and an adaptive neuro-fuzzy inference system (ANFIS), the study evaluated model performance using root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). The assessment revealed that the ANN trained with Bayesian regularization emerged as the most effective, boasting the lowest errors (RMSE = 0.00147, MSE = 0.0005, MAE = 0.0112) and the highest R2 (0.97), ensuring superior precision. Additionally, ANN trained with Levenberg–Marquardt and ANFIS exhibit commendable performance, showcasing minimal errors and high R2 values. Among the non-ANN models, boosted tree displayed a lower RMSE (0.08246) and a higher R2 (0.62), while a linear SVM demonstrated balanced performance with RMSE of 0.0877 and R2 of 0.57.