Summary
This study developed and validated improved water quality assessment models for marine environments by incorporating machine learning approaches. The research demonstrated that dual WQI methodologies, when enhanced with machine learning, provide more reliable and spatially-temporally resolved assessments of marine water quality than conventional approaches, thereby reducing uncertainty in environmental monitoring outcomes.
UK applicability
The methodology could support UK marine monitoring programmes under the Marine Strategy Framework Directive and Water Framework Directive, though applicability depends on whether the model was calibrated for UK coastal waters and validated against existing UK monitoring standards.
Key measures
Water quality index (WQI) scores; spatio-temporal resolution of waterbodies; uncertainty quantification in WQI assessment
Outcomes reported
The study evaluated water quality index (WQI) approaches for assessing marine waters using machine learning techniques. Both WQI methodologies demonstrated effective assessment of marine water quality with improved spatio-temporal resolution and reduced uncertainty in scoring.
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