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

Abstract 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.