An Integrated Water Quality and Water Theft Assessment System Using Machine Learning and Embedded IoT

One of the most fundamental requirements for human survival is the availability of safe drinking water, and with the global population increasing at an exponential rate, it is more important than ever to ensure a functional system. The conventional method for collecting data on water quality involves physically collecting samples and transporting them to a laboratory for examination. However, due to the significant effort and resources required, this method becomes impractical. The purpose of this paper was to build, test, and assess how well machine learning (ML) as well as the Internet of Things (IoT) work at water distribution and storage stations. In the beginning, we built a model of the system and tested it with classifiers and reliability matrices to see how well it worked. In order to determine the amount of contaminants in drinking water, this study takes into account the physical and chemical properties of water. We measure characteristics, for instance, temperature, pH, turbidity, conductivity (Formula: see textS/cm), dissolved oxygen (DO), and chlorine level (mg/L). We analyzed the data from the sensors and used various ML algorithms to forecast the amount of impurities in the water. From the outcomes, the random forest (RF) model is the best option for predicting the source and state of water since it has the maximum evaluation measures (89% Formula: see text1-score and accuracy). With the goal of automating identification based on water conductivity flow, we also developed a water theft detection tool. Results from the system led us to the conclusion that artificial intelligence and IoT are superior for remotely monitoring water quality, both safe and dangerous.