Grupo UTN GISTAQ (Grupo de Investigación sobre temas Ambientales y Químicos)

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Author: search.filters.author.Hervot, Elsa IvonneSubject: search.filters.subject.random forest

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    Turbidity estimation by machine learning modelling and remote sensing techniques applied to a water treatment plant
    (International Centre for Sustainable Development of Energy, Water and Environment Systems SDEWES, 2025-06-01) Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro Rubén
    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.
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    Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
    (2024-01-14) Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro Rubén
    Water availability and sanitation are among the UN Sustainable Development goals for 2030. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. Clean water is a scarce resource fundamental for human development and well-being. Treatment plants depend on the current water quality state to properly provide clean water. Combining laboratory measurements, provided by a water plant in Resistencia city, Argentina, and remote sensing data, i.e., surface reflectance, from Sentinel-2 platform, several algorithms were developed, trained, and compared for turbidity estimation. The model with the highest performance metrics was a random forest model, with Pearson’s coefficient of determination (R2) 0.918 and root-mean squared error (RMSE) 138.8 nephelometric turbidity units (NTU). Global feature importance and partial dependencies profiles techniques were applied to the random forest model to understand the spectral bands effects. Turbidity maps and time series were made and analyzed.