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

arXiv: https://arxiv.org/abs/2202.05664 · HTML: https://mlsanity.com/paper/2202.05664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis](https://mlsanity.com/paper/2107.03230/) (2021)

## Abstract (arXiv)

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.