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 p...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daniela Džal, Ivana Nižetić Kosović, Toni Mastelić, Damir Ivanković, Tatjana Puljak, Slaven Jozić
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	faecal indicator bacteria <i>E. coli</i> intestinal enterococci bathing water quality prediction predictive models neural network Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/2f7ba5e9da9c48cb9222174cf68d615b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2f7ba5e9da9c48cb9222174cf68d615b
record_format	dspace
spelling	oai:doaj.org-article:2f7ba5e9da9c48cb9222174cf68d615b2021-11-11T19:54:16ZModelling Bathing Water Quality Using Official Monitoring Data10.3390/w132130052073-4441https://doaj.org/article/2f7ba5e9da9c48cb9222174cf68d615b2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3005https://doaj.org/toc/2073-4441Predictive 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 <i>Escherichia coli</i> 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%.Daniela DžalIvana Nižetić KosovićToni MastelićDamir IvankovićTatjana PuljakSlaven JozićMDPI AGarticlefaecal indicator bacteria<i>E. coli</i>intestinal enterococcibathing water quality predictionpredictive modelsneural networkHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3005, p 3005 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	faecal indicator bacteria <i>E. coli</i> intestinal enterococci bathing water quality prediction predictive models neural network Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	faecal indicator bacteria <i>E. coli</i> intestinal enterococci bathing water quality prediction predictive models neural network Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Daniela Džal Ivana Nižetić Kosović Toni Mastelić Damir Ivanković Tatjana Puljak Slaven Jozić Modelling Bathing Water Quality Using Official Monitoring Data
description	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 <i>Escherichia coli</i> 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%.
format	article
author	Daniela Džal Ivana Nižetić Kosović Toni Mastelić Damir Ivanković Tatjana Puljak Slaven Jozić
author_facet	Daniela Džal Ivana Nižetić Kosović Toni Mastelić Damir Ivanković Tatjana Puljak Slaven Jozić
author_sort	Daniela Džal
title	Modelling Bathing Water Quality Using Official Monitoring Data
title_short	Modelling Bathing Water Quality Using Official Monitoring Data
title_full	Modelling Bathing Water Quality Using Official Monitoring Data
title_fullStr	Modelling Bathing Water Quality Using Official Monitoring Data
title_full_unstemmed	Modelling Bathing Water Quality Using Official Monitoring Data
title_sort	modelling bathing water quality using official monitoring data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2f7ba5e9da9c48cb9222174cf68d615b
work_keys_str_mv	AT danieladzal modellingbathingwaterqualityusingofficialmonitoringdata AT ivananizetickosovic modellingbathingwaterqualityusingofficialmonitoringdata AT tonimastelic modellingbathingwaterqualityusingofficialmonitoringdata AT damirivankovic modellingbathingwaterqualityusingofficialmonitoringdata AT tatjanapuljak modellingbathingwaterqualityusingofficialmonitoringdata AT slavenjozic modellingbathingwaterqualityusingofficialmonitoringdata
_version_	1718431339991007232

Modelling Bathing Water Quality Using Official Monitoring Data

Ejemplares similares