An Effective Smart Water Quality Monitoring and Management System Using IoT and Machine Learning

Water is a fundamental and essential requirement for human existence, as nearly 70% of our body is constituted with water. Consumption of deteriorated water quality can lead to the cause of various life-threatening diseases such as Cholera, typhoid, etc. Annually, an estimated 3.4 million individuals die from drinking polluted water. Despite numerous technological advancements, traditional methods continue to be employed for monitoring water quality. These methods are very inefficient as they are quite time-consuming, expensive, and cannot provide real-time information for monitoring water quality. Therefore, this article suggested a model designed on the Internet of Things (IoT) that addresses the existing underlying water quality issues and could replace the conventional way of water monitoring systems. To check the water quality parameters, several sensors (SNs) have been used to collect the real-time data and transfer further for analysis purposes via a range of machine learning techniques, including XGBoost, random forest, AdaBoost, and decision tree. These methods exhibit robust performance in terms of accuracy, precision, recall, and f1 score. Through the combination of the IoT and ML, the proposed real-time water quality monitoring (WQM) system offers continuous monitoring, analysis, and prediction of water quality parameters. The integration of these technologies and outcomes of experimental works prove that the proposed model can help to safeguard the availability of potable and clean water resources for present and future generations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.