Explaining and Predicting Microbiological Water Quality for Sustainable Management of Drinking Water Treatment Facilities

Goran Volf (), Ivana Sušanj Čule, Nataša Atanasova, Sonja Zorko and Nevenka Ožanić
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Goran Volf: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Ivana Sušanj Čule: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Nataša Atanasova: Department of Environmental Civil Engineering, Faculty of Civil and Geodetic Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Sonja Zorko: Istarski Vodovod d.o.o., 52420 Buzet, Croatia
Nevenka Ožanić: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia

Sustainability, 2025, vol. 17, issue 15, 1-26

Abstract: The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health.

Keywords: Butoniga reservoir; Butoniga DWTF; microbiological water quality; physico-chemical parameters; total coliforms; E. coli bacteria; prediction; machine learning; sustainable management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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