Comparative Study on Water Potability Prediction using Ensembled Based Techniques - Akshatha, M. R.; Chitra, K. (INTI International University, 2025)
Water quality assessment plays a vital role in public health protection and environmental sustainability. Conventional testing techniques, though accurate, are time-consuming, labour-intensive, and prone to human error. Recent advancements in Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) have transformed water potability prediction through intelligent, automated systems. This paper presents a comparative review of ensemble and hybrid ML/DL approaches such as Bagging, Gradient Boosting, XGBoost, and stacked models that have achieved accuracies ranging from 83% to 99.6% in recent studies between 2023 to 2025. Furthermore, IoT-based sensors and blockchain integration enable real-time monitoring, transparency, and data security in water management frameworks. This work highlights current trends, research gaps, and emerging innovations focusing on adaptive, scalable, and secure water quality prediction systems for sustainable smart water management.