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

Abstract

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R 2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial E.Coli and enterococci models achieved strong R 2 values of 0.85 and 0.83, while the temporal models achieved R 2 values of 0.74 and 0.67. The temporal model also achieved moderate R 2 values of 0.44 and 0.46 at a site with historically high coastal water quality.

Impacts of Atmospheric and Anthropogenic Factors on Microbiological Pollution of the Recreational Coastal Beaches Neighboring Shipping Ports

“…[ 3 , 4 , 5 , 6 , 7 ]. Microbial contamination prediction methods prove to be a feasible tool in water quality control [ 8 , 9 ]. Additionally, during the assessment of microbiological pollution sources, the sediment and the beach sand are only sporadically examined, although the data indicate that this habitat could serve as an important reservoir of microorganisms [ 10 , 11 , 12 , 13 ].…”