Internet of Things-Enabled Water Quality Monitoring System

Water quality monitoring protects human health, aquatic habitats, and sustainable water resource management. Manual sampling and laboratory testing are laborious, time-consuming, and unsuited for continuous water quality monitoring. A proof-of-concept IoT-Enabled Water Quality Monitoring System (IoT-WQMS) that uses an IoT-based sensor architecture and machine learning for intelligent water quality evaluation is presented in this work. The framework predicts water potability using pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The publicly available Kaggle Water Potability dataset, comprising 3,276 water samples, was used for experimental assessment. After median-based missing-value imputation, Min-Max normalization, and an 80:20 training-testing split, performance assessment was performed. The suggested prediction model has 98.21% accuracy, 97.86% precision, 98.04% recall, 97.95% F1-score, and a receiver operating characteristic area under the curve (ROC-AUC) of 0.991. The model showed steady and dependable performance with an average prediction latency of 0.18 s per sample and a mean accuracy of 97.94% ± 0.42% after 10-fold cross-validation. Scalability investigation indicated that increasing the dataset size from 20% to 100% increased execution time from 1.8 s to 7.9 s while keeping prediction accuracy above 97.5%. An ESP32 microcontroller, low-cost sensing modules, and cloud-based processing enable an economically viable framework for intelligent water quality assessment, laying the groundwork for future real-time IoT deployment and sustainable environmental monitoring.