Comparative Study of Water Quality Prediction across Domains using Machine Learning

Maintaining water quality is essential for environmental sustainability and public health. This study evaluates three major approaches to water quality assessment: conventional laboratory testing, Machine Learning (ML)-based models, and Internet of Things (IoT)-enabled real-time monitoring. While traditional techniques provide reliable measurements, they are often time-consuming and resource-intensive. ML methods offer advanced predictive capabilities and pattern recognition but require high-quality datasets and technical expertise, whereas IoT frameworks enable continuous remote monitoring with instant data access. This work conducts a comparative analysis to highlight the strengths and limitations of these approaches and contributes by systematically evaluating ML techniques across three geographically diverse datasets. Furthermore, the study investigates cross-domain generalizability by training models on one dataset and testing on entirely different datasets, thereby assessing their adaptability to varied environmental and geographical contexts.