Explaining and predicting microbiological water quality for sustainable management of drinking water treatment facilitiesVolf, Goran (Avtor) Sušanj Čule, Ivana (Avtor) Atanasova, Nataša (Avtor) Zorko, Sonja (Avtor) Ožanić, Nevenka (Avtor) Butoniga reservoirButoniga DWTFmicrobiological water qualityphysico-chemical parameterstotal coliformsE. coli bacteriapredictionmachine learningsustainable managementThe 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 a machine learning tool for the induction of models to describe and predict microbiological water quality fo 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 f actors that influence the microbiological quality of water. 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 model trees due to 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 predictive microbiological conditions in the Butoniga reservoir.20252025-08-04 10:10:07Članek v reviji171093sl