Predictive modeling of microbiological seawater quality classification
in karst region using cascade model
- URL: http://arxiv.org/abs/2202.05664v1
- Date: Fri, 11 Feb 2022 15:03:31 GMT
- Title: Predictive modeling of microbiological seawater quality classification
in karst region using cascade model
- Authors: Ivana Lu\v{c}in, Sini\v{s}a Dru\v{z}eta, Goran Mau\v{s}a, Marta Alvir,
Luka Grb\v{c}i\'c, Darija Vuki\'c Lu\v{s}i\'c, Ante Sikirica, Lado
Kranj\v{c}evi\'c
- Abstract summary: In this paper, an in-depth analysis of Escherichia coli seawater measurements during the bathing season in Rijeka, Croatia was conducted.
Submerged sources of groundwater were observed at several measurement locations which could be the cause for increased E. coli values.
A cascade machine learning model is used to predict coastal water quality based on meteorological data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, an in-depth analysis of Escherichia coli seawater measurements
during the bathing season in the city of Rijeka, Croatia was conducted.
Submerged sources of groundwater were observed at several measurement locations
which could be the cause for increased E. coli values. This specificity of
karst terrain is usually not considered during the monitoring process, thus a
novel measurement methodology is proposed. A cascade machine learning model is
used to predict coastal water quality based on meteorological data, which
improves the level of accuracy due to data imbalance resulting from rare
occurrences of measurements with reduced water quality. Currently, the cascade
model is employed as a filter method, where measurements not classified as
excellent quality need to be further analyzed. However, with improvements
proposed in the paper, the cascade model could be ultimately used as a
standalone method.
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