Ground Water Quality Analysis Using Machine Learning Techniques

Abstract: The study explores the application of IOT sensors, including pH, turbidity, conductivity, temperature, and humidity, for sampling water from diverse sources. By leveraging these sensors, the research aims to predict water portability using the random forest algorithm. This approach involves training the model with existing datasets and subsequently testing it on samples collected via IOT sensors. The abstract suggests that such an approach could provide insights into efficient and accurate methods for assessing water quality in both confined and open water systems. Additionally, comparative analysis with other machine learning algorithms may further elucidate the optimal method for determining water portability.

Keywords: Water Quality, pH, turbidity, conductivity, Random Forest algorithm, PyCharm IDE, sensors.