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.

In order to improve the treatment processes of the drinking water treatment plant (DWTP) located near the Butoniga reservoir in Istria (Croatia), a prediction of the water quality index (WQI) was done. Based on parameters such as temperature, pH, turbidity, KMnO₄, NH₄, Mn, Al and Fe, the calculation of WQI was conducted, while for the WQI prediction models, along with the mentioned parameters, O₂, TOC and UV254 were additionally used. Four models were built to predict WQI with a time step of one, five, ten, and fifteen days in advance, in order to improve treatment processes of the DWTP regarding the changes in raw water quality in the Butoniga reservoir. Therefore, obtained models can help in the optimization of treatment processes, which depend on the quality of raw water, and overall, in the sustainability of the treatment plant. Results showed that the obtained correlation coefficients for all models are relatively high and, as expected, decrease as the number of prediction days increases; conversely, the number of rules, and related linear equations, depends on the parameters set in the WEKA modelling software, which are set to default settings which give the highest values of correlation coefficient (R) for each model and the optimal number of rules. In addition, all models have high accuracy compared to the measured data, with a good prediction of the peak values. Therefore, the obtained models, through the prediction of WQI, can help to manage the treatment processes of the DWTP, which depend on the quality of raw water in the Butoniga reservoir.

With climate change and increasing summer tourism in Croatia, the protection and sustainable management of natural freshwater resources, such as lakes, are becoming crucial. This research aims to develop a predictive hydrological model that can forecast water levels in Vrana Lake to serve as a tool for the sustainable management of water supply systems. Therefore, in this paper, a data-driven predictive model based on an artificial neural network (ANN) is implemented. For this purpose, the multilayer perceptron (MLP) ANN architecture is chosen. For model development, the monthly data of rainfall amount, evaporation, losses, water supply pumping, and lake water levels at Vrana Lake from the years 1954–2022 were used, and the model for water level prediction is developed for time prediction steps: (i) Δt = 1 month, (ii) Δt = 2 months, (iii) Δt = 4 months, and (iv) Δt = 6 months. The model quality assessment indicated strong prediction capabilities for time steps of Δt = 1 month and Δt = 2 months. However, the models for time steps of Δt = 4 months and Δt = 6 months exhibited lower quality. Despite this, they can still serve as valid indicators for predicting trends in water level fluctuations.