METHOD FOR MONITORING THE PHYSICAL AND CHEMICAL PROPERTIES OF LIQUIDS BASED ON MACHINE LEARNING

This article presents an approach developed for real-time monitoring of the physical and chemical parameters of drinking water using machine learning (ML) algorithms and Attenuated Total Reflection (ATR) spectroscopy. Although traditional chemical analysis methods provide high accuracy, they are time- and resource-intensive and do not allow automated monitoring. The proposed methodology employs spectral absorption values combined with multi-output regression (Multioutput Regressor) and the XGBoost model to simultaneously predict DOC, NH₄, PO₄, SO₄, NO₃, and NO₂ parameters. The results demonstrate that the XGBoost algorithm ensures the highest accuracy and stability, making it a reliable approach for assessing drinking water quality.