Original scientific paper
https://doi.org/10.13044/j.sdewes.d13.0539
Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
Víctor Gauto
orcid.org/0000-0001-9960-8558
; National Technological University Faculty of Resistencia, Resistencia, Argentina
Enid Utges
; Universidad Tecnológica Nacional, Resistencia, Argentina
Elsa Hervot
; National Technological University Faculty of Resistencia, Resistencia, Argentina
Maria Daniela Tenev
; Universidad Tecnológica Nacional, Resistencia, Argentina
Alejandro Farías
; Universidad Tecnológica Nacional, Resistencia, Argentina
Abstract
Clean water is a scarce resource, fundamental for human development and well-being. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. In situ sampling is an essential and labour-intensive task with high costs. As an alternative, a large water quality dataset from a potabilisation plant can be beneficial to this step. Combining laboratory measurements from a water treatment plant in North-East Argentina and spectral data from the Sentinel-2 satellite platform, several regression algorithms were proposed, trained, and compared for turbidity estimation at the plant inlet water in a local river. The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). Global feature importance and partial dependencies profile techniques identified the most influential spectral bands. Maps and histograms were made to explore the spatial distribution of turbidity.
Keywords
Random forest; Remote sensing; Sentinel-2; Turbidity; Water quality
Hrčak ID:
332972
URI
Publication date:
5.12.2025.
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