Predictive modeling of microbiological seawater quality classification in karst region using cascade model
This work addresses water quality monitoring for public health in karst coastal areas, where groundwater sources are often overlooked, but it is incremental as it builds on existing cascade models with specific improvements.
The paper tackled predicting microbiological seawater quality in a karst region by analyzing E. coli measurements and proposing a cascade machine learning model using meteorological data, which improved accuracy by addressing data imbalance from rare poor-quality events.
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.