Computer System for Forecasting Water Quality Parameters Based on Machine Learning

Abstract

The article presents research on environmental forecasting using machine learning methods for water quality analysis in the Southern Bug River. It focuses on the application of binary logistic regression and random forest regression to estimate the effects of various environmental parameters on key water quality indicators. The study involved data preparation by reading and processing CSV files with economic and environmental indicators and generating feature matrices and target vectors for model training. Both models were trained using nine years of data. The results can be used to develop water resource management strategies, predict the ecological state of water basins, and assess the influence of anthropogenic factors on the hydrochemical regime of rivers. In addition, the revealed relationships between the hydrochemical characteristics of water contribute to increasing the automation of water quality monitoring processes.