Optimizing water quality index using machine learning: a six-year comparative study in riverine and reservoir systems.

ABSTRACT: Human activities and climate change are intensifying threats to water quality, necessitating scientifically robust assessments to strengthen water resource management and ensure safe drinking water. However, the global application of WQI has increased significantly while facing persistent uncertainties in parameter weighting, aggregation functions, and model transparency. In this study, a comparative optimization framework using three machine learning algorithms, five weighting methods, and eight aggregation functions, was proposed for water quality index (WQI) model improvement. Two typical surface water systems, i.e., riverine and reservoir water bodies, were analyzed using six-year monthly data (2017-2022) from 31 sites in a typical mega hydro-engineering, the Danjiangkou Reservoir (DJK

SUBMITTER: Yuan P 

PROVIDER: S-EPMC12484736 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature