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

• URL: http://arxiv.org/abs/2107.03230v2
• Date: Fri, 9 Jul 2021 07:09:03 GMT
• Title: Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis
• Authors: Luka Grb\v{c}i\'c, Sini\v{s}a Dru\v{z}eta, Goran Mau\v{s}a, Tomislav Lipi\'c, Darija Vuki\'c Lu\v{s}i\'c, Marta Alvir, Ivana Lu\v{c}in, Ante Sikirica, Davor Davidovi\'c, Vanja Trava\v{s}, Daniela Kalafatovi\'c, Kristina Pikelj, Hana Fajkovi\'c, Toni Holjevi\'c and Lado Kranj\v{c}evi\'c
• Abstract summary: Poor coastal water quality can harbor pathogens that are dangerous to human health. Routine monitoring data of $Escherichia Coli$ and enterococci across 15 public beaches in Rijeka, Croatia, were used to build machine learning models. Catboost algorithm performed best with R$2$ values of 0.71 and 0.68 for predicting $E. Coli$ and enterococci.
• Score: 1.1124907412872893
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Coastal water quality management is a public health concern, as poor coastal water quality can 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 relationships with environmental stressors. Gradient Boosting (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with 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 accuracy of both ML models were examined at sites with the 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 high coastal water quality.

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