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<p>River ecosystems are increasingly impacted by human activities such as industrial development and agriculture, leading to shifts in water quality variables and threatening ecological sustainability. Here, we present a modeling approach that integrates reaeration processes with hydraulic parameters to improve prediction of the Water Quality Index in river systems. We developed a genetic programming model to estimate the reaeration coefficient, expressed as a second-order rate constant, and use it as a key predictor of water quality. The model shows that this coefficient can be reliably derived from the Froude number, a dimensionless indicator of flow regime. Through iterative regression, less influential variables are removed, resulting in a simplified equation that maintains high predictive accuracy. Turbidity, temperature, and the reaeration coefficient are identified as primary drivers of water quality. The proposed framework is computationally efficient, cost-effective, and suitable for real-time monitoring across diverse river systems.</p>

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Instant water quality index prediction via reaeration process and hydraulic parameters in the river system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月28日

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  • Amin Arzhangi,
  • Sadegh Partani,
  • Ali Danandeh Mehr,
  • Faezeh Ezzati,
  • Ali Saber

摘要

River ecosystems are increasingly impacted by human activities such as industrial development and agriculture, leading to shifts in water quality variables and threatening ecological sustainability. Here, we present a modeling approach that integrates reaeration processes with hydraulic parameters to improve prediction of the Water Quality Index in river systems. We developed a genetic programming model to estimate the reaeration coefficient, expressed as a second-order rate constant, and use it as a key predictor of water quality. The model shows that this coefficient can be reliably derived from the Froude number, a dimensionless indicator of flow regime. Through iterative regression, less influential variables are removed, resulting in a simplified equation that maintains high predictive accuracy. Turbidity, temperature, and the reaeration coefficient are identified as primary drivers of water quality. The proposed framework is computationally efficient, cost-effective, and suitable for real-time monitoring across diverse river systems.