Global water quality is increasingly threatened by industrial expansion, urbanization and climate change, necessitating robust and adaptive monitoring systems. This review critically examines the evolution of water quality assessment methodologies, transitioning from traditional field-based approaches to advanced technologies such as Artificial Intelligence (AI), constructed wetlands, remote sensing and Internet of Things (IoT)-enabled monitoring systems. Emphasis is placed on the evaluation of key water quality parameters, including physicochemical, bacteriological and heavy metal contaminants, across both surface and groundwater systems. Analytical tools such as the Water Quality Index (WQI), Geographic Information Systems (GIS), hydrus modelling, Multivariate Statistical Analysis (MSA) and regression-based machine learning models-including Artificial Neural Networks (ANN) and Hammerstein-Wiener (HW) models are reviewed in terms of their applicability and effectiveness. Bibliometric analysis is employed to uncover current research trends, interdisciplinary linkages and geographic hotspots in water quality research. The primary objective of this review is to synthesize current advancements, identify methodological gaps and propose an integrated framework that combines traditional techniques with modern analytics to enhance sustainable water resource management strategies.
Barath et al. (Fri,) studied this question.