Towards Predictive Water Quality: Synergies Between Machine Learning and Internet of Things

Water, as an essential element for life, holds paramount importance, its presence is fundamental for the survival of all forms of life. The challenges related to the safety and accessibility of drinking water represent urgent global issues. In this study, we introduce a method for analyzing water quality that leverages the capabilities of machine learning and the Internet of Things (IoT). We assume that IoT sensors monitor the levels of pH, hardness, solids, chloramines, sulfate, conductivity, organic matter, trihalomethanes, and turbidity to assess water potability. The data recorded by these sensors are then stored in a database and subjected to a thorough analysis. As part of our study aiming to predict water quality, we explored four distinct approaches using different machine learning methods: the deep learning method, classification, regression, and clustering. The primary objective was to determine which of these methods proved most effective for our specific dataset. After calculating accuracy rates for each method, a detailed comparison was conducted to identify the most performant approach. The advantages and disadvantages of each method were taken into account, and recommendations were formulated based on the obtained results. In summary, this comprehensive study aims to provide in-depth insights into the relative effectiveness of these four machine learning approaches in predicting water quality, thereby contributing to guiding future applications in this critical domain.