Enhancing source water quality predictions to improve treatment by integrating watershed data on water quality, river flow and rainfall into interpretable machine learning algorithms

Christian Ortiz-Lopez; C. Bouchard; Manuel J. Rodríguez

doi:10.1039/d6ew00235h

January 1, 2026Environmental Science Water Research & TechnologyOpen Access

Enhancing source water quality predictions to improve treatment by integrating watershed data on water quality, river flow and rainfall into interpretable machine learning algorithms

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Key Points

The aim is to enhance predictions of source water quality by integrating watershed data.
Integrated watershed data on water quality, river flow, and rainfall.
Utilized interpretable machine learning algorithms.
Demonstrated improvements in predicting raw water quality during rainfall events.
Achieved higher accuracy in identifying changes in turbidity related to river flow fluctuations.

Implication

Explores improved water quality predictions using watershed data, suggesting enhanced treatment efficiency.

Abstract

Raw water quality used for municipal water treatment is impacted during and after rainfall events and the resultant changes in river flow. Recently, raw water quality parameters such as turbidity...

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Cite This Study

Ortiz-Lopez et al. (2026) studied this question.

synapsesocial.com/papers/6a323b21d50b63ecad205cd4 https://doi.org/10.1039/d6ew00235h

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Enhancing source water quality predictions to improve treatment by integrating watershed data on water quality, river flow and rainfall into interpretable machine learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

Enhancing source water quality predictions to improve treatment by integrating watershed data on water quality, river flow and rainfall into interpretable machine learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

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