Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Predictive modeling of microbiological seawater quality in karst region using cascade model (CROSBI ID 313745)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Lučin, Ivana ; Družeta, Siniša ; Mauša, Goran ; Alvir, Marta ; Grbčić, Luka ; Vukić Lušić, Darija ; Sikirica, Ante ; Kranjčević, Lado Predictive modeling of microbiological seawater quality in karst region using cascade model // Science of the total environment, 851 (2022), 158009, 13. doi: 10.1016/j.scitotenv.2022.158009

Podaci o odgovornosti

Lučin, Ivana ; Družeta, Siniša ; Mauša, Goran ; Alvir, Marta ; Grbčić, Luka ; Vukić Lušić, Darija ; Sikirica, Ante ; Kranjčević, Lado

engleski

Predictive modeling of microbiological seawater quality in karst region using cascade model

This paper presents an in-depth analysis of seawater quality measurements during the bathing seasons from year 2009 to 2020 in the city of Rijeka, Croatia. Due to rare occurrences of measurements with less than excellent water quality, considered dataset is deeply imbalanced. Additionally, it incorporates measurements under the influence of submerged groundwater discharges (SGD), which were observed in some bathing locations. These discharges were previously thought to dry up during the summer season and are now suspected to be one of the causes of increased Escherichia coli values. Consequently, and in view of the fact that the accuracy of prediction models can be significantly influenced by temporal and spatial variation of the input data, a novel cascade prediction modeling strategy was proposed. It consists of a sequence of prediction models which tend to identify general environmental conditions which confidently lead to excellent bathing water quality. The proposed model uses environmental features which can rather easily be estimated or obtained from the weather forecast. The model was trained on a highly biased dataset, consisting of data from locations with and without SGD influence, and for the time period spanning extremely dry and warm seasons, extremely wet seasons, as well as normal seasons. To simulate realistic application, the model was tested using temporal and spatial stratification of data. The cascade strategy was shown to be a good approach for reliably detecting environmental parameters which produce excellent water quality. Proposed model is designed as a filter method, where instances classified as less-than-excellent water quality require further analysis. The cascade model provides great flexibility as it can be customized to the particular needs of the investigated area and dataset specifics.

Bathing water quality Machine learning Fecal pollution Cascade prediction modeling Karst region Submerged groundwater discharge

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o izdanju

851

2022.

158009

13

objavljeno

0048-9697

10.1016/j.scitotenv.2022.158009

Povezanost rada

Interdisciplinarne biotehničke znanosti, Interdisciplinarne tehničke znanosti, Javno zdravstvo i zdravstvena zaštita, Temeljne tehničke znanosti

Poveznice
Indeksiranost