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<p>The world’s median population is projected to reach 8.8 billion by 2050, making water management, especially groundwater, increasingly important. This research article seeks to address the pressing need for an effective method of evaluating and forecasting groundwater quality. Traditionally, water quality testing methods entail significant experimentation, a time-consuming procedure. To overcome this challenge in an alternative way, our proposed methodology involves parameterizing groundwater physicochemical parameters (Q-value) using machine learning algorithms. Focusing on groundwater samples collected from the Russell River of Australia between December 2016 and April 2020 (scaled to approximately 1300 datapoints on average), this research article uses the concept of <i>Q</i> value, a standardized measure facilitating comprehensive water quality assessment. This study significantly contributes to sustainable water resource management by providing a comprehensive examination of groundwater quality through the utilization of deep learning algorithms. The proposed models, namely Conv-LSTM with Attention, Conv-Bi-LSTM with Attention, LSTM with Attention, and Bi-LSTM with Attention, not only offer a distinctive framework for forecasting <i>Q</i> values but also serve as essential tools for timely decision making in water resource distribution. Among these, the Bidirectional LSTM with Attention model achieved the highest predictive accuracy, with a root mean square error of 0.0057, a mean absolute error of 0.0022, a symmetric mean absolute percentage error of 3.8875%, and a coefficient of determination of 0.9910. These results demonstrate its effectiveness in capturing variability and accurately explaining observed trends in groundwater quality. The proposed framework is a reliable, scalable, and timely decision-support tool for water resource management and policy making.</p>

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Ground water quality assessment and forecasting using attention-based mechanisms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Shyam Sundhar,
  • S. Shashannk,
  • D. Nandhini,
  • S. Amutha

摘要

The world’s median population is projected to reach 8.8 billion by 2050, making water management, especially groundwater, increasingly important. This research article seeks to address the pressing need for an effective method of evaluating and forecasting groundwater quality. Traditionally, water quality testing methods entail significant experimentation, a time-consuming procedure. To overcome this challenge in an alternative way, our proposed methodology involves parameterizing groundwater physicochemical parameters (Q-value) using machine learning algorithms. Focusing on groundwater samples collected from the Russell River of Australia between December 2016 and April 2020 (scaled to approximately 1300 datapoints on average), this research article uses the concept of Q value, a standardized measure facilitating comprehensive water quality assessment. This study significantly contributes to sustainable water resource management by providing a comprehensive examination of groundwater quality through the utilization of deep learning algorithms. The proposed models, namely Conv-LSTM with Attention, Conv-Bi-LSTM with Attention, LSTM with Attention, and Bi-LSTM with Attention, not only offer a distinctive framework for forecasting Q values but also serve as essential tools for timely decision making in water resource distribution. Among these, the Bidirectional LSTM with Attention model achieved the highest predictive accuracy, with a root mean square error of 0.0057, a mean absolute error of 0.0022, a symmetric mean absolute percentage error of 3.8875%, and a coefficient of determination of 0.9910. These results demonstrate its effectiveness in capturing variability and accurately explaining observed trends in groundwater quality. The proposed framework is a reliable, scalable, and timely decision-support tool for water resource management and policy making.