Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis.

Coastal water quality management is a public health concern, as water of poor quality can potentially harbor dangerous pathogens. In this study, we employ routine monitoring data of EscherichiaColi and enterococci across 15 beaches in the city of Rijeka, Croatia, to build machine learning models for predicting E.Coli and enterococci based on environmental features. Cross-validation analysis showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.69 for predicting E.Coli and enterococci, respectively, compared to other evaluated algorithms. SHapley Additive exPlanations technique showed that salinity is the most important feature for forecasting both E.Coli and enterococci levels. Furthermore, for low water quality sites, the spatial predictive models achieved R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model achieved moderate R2 values of 0.44 and 0.46 at a site with high water quality.