Development of a Device and Software Solution for High-Accuracy Assessment and Monitoring of Groundwater Hydrogeochemical Parameters Based on the K-Nearest Neighbors (KNN) Machine Learning Algorithm

In this article, a novel device and software solution were developed for high-precision measurement and analysis of groundwater hydrogeochemical parameters, including pH, electrical conductivity, total dissolved solids (TDS), temperature, and other indicators. The proposed solution integrates the K-Nearest Neighbors (KNN) machine learning algorithm for data processing and enables real-time monitoring. The device operates using modern sensors, microcontrollers, and radio communication modules, while the software performs intelligent analysis of the collected data, providing visualization and predictive capabilities. This approach ensures high accuracy in measuring hydrogeochemical parameters and serves as an effective solution for sustainable management of groundwater resources. Groundwater can become unfit for human consumption due to various sources of pollution. Therefore, it is necessary to sustainably manage groundwater resources, conduct a comprehensive geochemical study of them, and regularly monitor their chemical evolution. In this study, a set of devices for determining groundwater parameters was developed and tested in wells based on optimized methods. The obtained samples were hydrogeochemically analyzed using machine learning algorithms.