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Open AccessArticle

Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches

1
Department of Earth and Environmental Sciences, School of Natural Sciences and Williamson Research Centre for Molecular Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
2
Departamento del Agua, Centro Universitario Regional Litoral Norte, Universidad de la República, Gral. Rivera 1350, Salto 50000, Uruguay
3
PDU Geología y Recursos Minerales, Centro Universitario Regional Este, Universidad de la República, Ruta 8 km 282, Treinta y Tres 33000, Uruguay
*
Authors to whom correspondence should be addressed.
Academic Editor: Domenico Cicchella
Water 2021, 13(4), 527; https://doi.org/10.3390/w13040527
Received: 20 December 2020 / Revised: 9 February 2021 / Accepted: 10 February 2021 / Published: 18 February 2021
Groundwater arsenic in Uruguay is an important environmental hazard, hence, predicting its distribution is important to inform stakeholders. Furthermore, occurrences in Uruguay are known to variably show dependence on depth and geology, arguably reflecting different processes controlling groundwater arsenic concentrations. Here, we present the distribution of groundwater arsenic in Uruguay modelled by a variety of machine learning, basic expert systems, and hybrid approaches. A pure random forest approach, using 26 potential predictor variables, gave rise to a groundwater arsenic distribution model with a very high degree of accuracy (AUC = 0.92), which is consistent with known high groundwater arsenic hazard areas. These areas are mainly in southwest Uruguay, including the Paysandú, Río Negro, Soriano, Colonia, Flores, San José, Florida, Montevideo, and Canelones departments, where the Mercedes, Cuaternario Oeste, Raigón, and Cretácico main aquifers occur. A hybrid approach separating the country into sedimentary and crystalline aquifer domains resulted in slight material improvement in a high arsenic hazard distribution. However, a further hybrid approach separately modelling shallow (<50 m) and deep aquifers (>50 m) resulted in the identification of more high hazard areas in Flores, Durazno, and the northwest corner of Florida departments in shallow aquifers than the pure model. Both hybrid models considering depth (AUC = 0.95) and geology (AUC = 0.97) produced improved accuracy. Hybrid machine learning models with expert selection of important environmental parameters may sometimes be a better choice than pure machine learning models, particularly where there are incomplete datasets, but perhaps, counterintuitively, this is not always the case. View Full-Text
Keywords: arsenic; groundwater; Uruguay; geostatistics; depth; geology arsenic; groundwater; Uruguay; geostatistics; depth; geology
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MDPI and ACS Style

Wu, R.; Alvareda, E.M.; Polya, D.A.; Blanco, G.; Gamazo, P. Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches. Water 2021, 13, 527. https://doi.org/10.3390/w13040527

AMA Style

Wu R, Alvareda EM, Polya DA, Blanco G, Gamazo P. Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches. Water. 2021; 13(4):527. https://doi.org/10.3390/w13040527

Chicago/Turabian Style

Wu, Ruohan; Alvareda, Elena M.; Polya, David A.; Blanco, Gonzalo; Gamazo, Pablo. 2021. "Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches" Water 13, no. 4: 527. https://doi.org/10.3390/w13040527

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