A Contamination Predictive Model for Escherichia coli in Rural Communities Dug Shallow Wells

• Published in: Sustainability Vol. 15; no. 3; p. 2408
• Main Authors: Lopes, Hítalo Tobias Lôbo, Baumann, Luis Rodrigo Fernandes, Scalize, Paulo Sérgio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Animal husbandry; Aquifers; Bayesian analysis; Contamination; Criteria; Data collection; E coli; Generalized linear models; Geology; Groundwater; Households; Microorganisms; Model accuracy; Prediction models; Rural areas; Rural communities; Shallow wells; Software; Statistical methods; Statistical models; Sustainability; Treated water; Turbidity; Variables; Water quality; Brazil
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15032408

In Brazilian rural communities, the lack of treated water leads their residents to seek individual and alternative solutions, in which dug shallow wells (DSW) are quite widespread. However, water quality may not be fitted for human consumption. For this reason, the current study aimed to predict the contamination of DSW water in rural communities in the Brazilian state of Goiás. For this, secondary data related to water quality, the distance to possible contamination sources, structural conditions, and local geology were evaluated. Therefore, a generalized linear model was applied, and its predictors were evaluated by stepwise methods (Akaike information criterion—AIC and Bayesian information criterion—BIC), generating an intermediate model. After the analysis, turbidity parameter was chosen to be removed resulting in a final, submitted to leave-one-out cross-validation method, and its performance was measured by a confusion matrix. The final model resulted in four predictive variables: well diameter, contour paving width, poultry, and swine husbandry existence. The model accuracy was 82.61%, with a true positive predictor of 82.18% and a negative predictor of 85.71%.