DEVICE AND SOFTWARE FOR ACCURATE MONITORING OF GROUNDWATER PARAMETERS USING KNN ALGORITHM

Abstract

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 quality can deteriorate due to pollution, making sustainable management and continuous monitoring essential. In this study, devices were developed and tested in wells to measure key parameters, and the collected samples were analyzed using machine learning algorithms.