Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

This study assessed seasonal variations in water quality and their impact on the Inyaka Dam and Crocodile River in Mpumalanga Province, South Africa. A total of 206 water samples were collected across four seasons to analyse parameters including pH, turbidity, electrical conductivity (EC), temperature, Manganese (Mn), Iron (Fe), phosphate (PO₄³⁻), nitrate-nitrogen (NO₃-N), E. coli, total coliforms, faecal streptococci, and Bifidobacteria. Principal component analysis (PCA) and Pearson correlation identified major pollutants and their seasonal variations, while cluster analysis grouped water quality by parameter fluctuations.

This review article intends to survey the information on water quality indices developed for the general evaluation of surface water and establish whether the water quality indices (WQIs) can be used to evaluate both lentic and lotic ecosystems simultaneously. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Waski & Parker index, and Hahn index are used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been revealed that despite the use of statistical methods such as cluster analysis (CA), factor analysis (FA), and analytic hierarchy process (AHP), WQI models continue to suffer from either eclipsing, ambiguity, or uncertainty limitations because natural ecosystems tend to be too complex for these statistical methods. This review thus recommends coupling statistical methods with machine learning techniques such as artificial neural networks (ANN) in the WQI model development processes.