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Pregled bibliografske jedinice broj: 1213328

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


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 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1213328 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

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

Izvornik
Science of The Total Environment (0048-9697) 851 (2022); 158009, 13

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

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



POVEZANOST RADA


Projekti:
EK-EFRR-KK.05.1.1.02.0017 - Računalni model strujanja, poplavljivanja i širenja onečišćenja u rijekama i obalnim morskim područjima (KLIMOD) (Kranjčević, Lado; Vukić Lušić, Darija; Davidović, Davor, EK - KK.05.1.1.02) ( CroRIS)

Ustanove:
Medicinski fakultet, Rijeka,
Tehnički fakultet, Rijeka

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

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 (međunarodna recenzija, članak, znanstveni)
Lučin, I., Družeta, S., Mauša, G., Alvir, M., Grbčić, L., Vukić Lušić, D., Sikirica, A. & Kranjčević, L. (2022) Predictive modeling of microbiological seawater quality in karst region using cascade model. Science of The Total Environment, 851, 158009, 13 doi:10.1016/j.scitotenv.2022.158009.
@article{article, author = {Lu\v{c}in, Ivana and Dru\v{z}eta, Sini\v{s}a and Mau\v{s}a, Goran and Alvir, Marta and Grb\v{c}i\'{c}, Luka and Vuki\'{c} Lu\v{s}i\'{c}, Darija and Sikirica, Ante and Kranj\v{c}evi\'{c}, Lado}, year = {2022}, pages = {13}, DOI = {10.1016/j.scitotenv.2022.158009}, chapter = {158009}, keywords = {Bathing water quality Machine learning Fecal pollution Cascade prediction modeling Karst region Submerged groundwater discharge}, journal = {Science of The Total Environment}, doi = {10.1016/j.scitotenv.2022.158009}, volume = {851}, issn = {0048-9697}, title = {Predictive modeling of microbiological seawater quality in karst region using cascade model}, keyword = {Bathing water quality Machine learning Fecal pollution Cascade prediction modeling Karst region Submerged groundwater discharge}, chapternumber = {158009} }
@article{article, author = {Lu\v{c}in, Ivana and Dru\v{z}eta, Sini\v{s}a and Mau\v{s}a, Goran and Alvir, Marta and Grb\v{c}i\'{c}, Luka and Vuki\'{c} Lu\v{s}i\'{c}, Darija and Sikirica, Ante and Kranj\v{c}evi\'{c}, Lado}, year = {2022}, pages = {13}, DOI = {10.1016/j.scitotenv.2022.158009}, chapter = {158009}, keywords = {Bathing water quality Machine learning Fecal pollution Cascade prediction modeling Karst region Submerged groundwater discharge}, journal = {Science of The Total Environment}, doi = {10.1016/j.scitotenv.2022.158009}, volume = {851}, issn = {0048-9697}, title = {Predictive modeling of microbiological seawater quality in karst region using cascade model}, keyword = {Bathing water quality Machine learning Fecal pollution Cascade prediction modeling Karst region Submerged groundwater discharge}, chapternumber = {158009} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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