A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components – selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation – with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved…