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Modelling Bathing Water Quality Using Official Monitoring Data (CROSBI ID 301086)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Džal, Daniela ; Nižetić Kosović, Ivana ; Mastelić, Toni ; Ivanković, Damir ; Puljak, Tatjana ; Jozić, Slaven Modelling Bathing Water Quality Using Official Monitoring Data // Water, 13 (2021), 21; 3005, 21. doi: 10.3390/w13213005

Podaci o odgovornosti

Džal, Daniela ; Nižetić Kosović, Ivana ; Mastelić, Toni ; Ivanković, Damir ; Puljak, Tatjana ; Jozić, Slaven

engleski

Modelling Bathing Water Quality Using Official Monitoring Data

Predictive models of bathing water quality are a useful support to traditional monitoring and provide timely and adequate information for the protection of public health. When developing models, it is critical to select an appropriate model type and appropriate metrics to reduce errors so that the predicted outcome is reliable. It is usually necessary to conduct intensive sampling to collect a sufficient amount of data. This paper presents the process of developing a predictive model in Kaštela Bay (Adriatic Sea) using only data from regular (official) bathing water quality monitoring collected during five bathing seasons. The predictive modelling process, which included data preprocessing, model training, and model tuning, showed no silver bullet model and selected two model types that met the specified requirements: a neural network (ANN) for Escherichia coli and a random forest (RF) for intestinal enterococci. The different model types are probably the result of the different persistence of two indicator bacteria to the effects of marine environmental factors and consequently the different die-off rates. By combining these two models, the bathing water samples were classified with acceptable performances, an informedness of 71.7%, an F-score of 47.1%, and an overall accuracy of 80.6%.

fecal indicator bacteria ; E. coli ; intestinal enterococci ; bathing water quality prediction ; predictive models ; neural network ; random forest

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Podaci o izdanju

13 (21)

2021.

3005

21

objavljeno

2073-4441

10.3390/w13213005

Povezanost rada

Biologija, Interdisciplinarne prirodne znanosti, Javno zdravstvo i zdravstvena zaštita, Računarstvo

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