Water Quality Assessment Through Predictive Modeling Employing Machine Learning Methods

Abstract

Water quality declines pose serious problems that need creative methods to ensure proper monitoring. In order to gather and evaluate water quality data in real time, this study presents the Water Quality Measurement Application, which integrates cutting-edge sensors with artificial intelligence (AI). By creating a tool that is easily accessed by environmentalists and scientists, the goal is to enhance existing techniques that depend on labor-intensive and less accurate manual sampling and analysis. The accuracy of current sensor-based systems is frequently restricted, and they are unable to accurately forecast problems with water quality. To get around this, machine learning (ML) techniques are used in the application to assess and forecast water quality situations while IoT sensors are integrated for continuous data collecting. Safely moving data to a cloud platform is made possible by the Blynk IoT framework, which guarantees accessibility and security. When it came to identifying water quality characteristics, the Random Forest (RF) approach outperformed other machine learning models, including K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Multinomial Naive Bayes (MNB). When compared to conventional techniques, this breakthrough yields forecasts that are more trustworthy. Subsequent efforts will concentrate on improving the application by extending the scope of observable metrics and adding user input. Accuracy improvement is another goal of ongoing research into ML algorithms. By providing a more sophisticated, automated method of comprehending and regulating water health, this creative solution benefits environmental professionals as well as labs.