TL;DR
An integrated framework that combines multivariate statistics and machine learning to enhance data-driven water quality management in Malaysia by incorporating statistical interpretation and predictive modelling is developed, enabling robust estimation of real-time water quality from routine monitoring data.
The findings demonstrate that a parsimonious machine-learning framework can capture meaningful water-quality variability from multidimensional environmental measurements and provides a practical basis for identifying influential indicators and supporting targeted water-quality monitoring.
This study examines whether engineered interaction effects among the six main parameters of the Malaysian Water Quality Index (WQI) can improve multi-class water quality classification. River monitoring data from the Department of Environment from 2020 to 2022 were pre-processed and statistically validated prior to mod...
Figure showing an interpretable framework for water quality parameters in tropical rivers Machine learning offers a promising approach for water quality assessment by modelling complex nonlinear relationships among physicochemical parameters. This study compares four machine learning classifiers. Artificial Neural N...
The Water Quality Index (WQI) is widely applied for long-term monitoring and inter-basin comparison of surface water quality; however, its interpretation may vary depending on basin characteristics and hydrological organization. This study evaluates WQI behaviour in three Lithuanian river basins (Nemunas, Mūša, and Ven...
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