RT Journal Article
T1 New insights into machine learning prediction techniques for real-time sanitary risk assessment in karst drinking water sources affected by faecal contamination
A1 Fernández Ortega, Jaime
A1 Barberá-Fornell, Juan Antonio
A1 Andreo-Navarro, Bartolomé
K1 Aguas subterráneas
K1 Acuíferos
K1 Agua potable - Contaminación
K1 Excrementos
K1 Detectores
AB Safe drinking water supply from karst aquifers faces several challenges due to their high vulnerability to contamination, which can result in abrupt water quality variations in matter of hours. The analysis of faecal bacterial activity requires time-consuming cultures and expensive reagents in the laboratory, which can delay the detection of an imminent contamination event. An innovative methodology is proposed in this research to overcome these limitations and provide real-time insights about water quality in drinking sources. Fieldwork activities included the continuous monitoring of water parameters (spring discharge, electrical conductivity, turbidity and Tryptophan-like fluorescence) and groundwater sampling for Escherichia coli determination in two springs draining a binary karst aquifer in S Spain during three hydrological years (2020/21 to 2022/23). Ten supervised Machine Learning models were then tested to infer five sanitary risk levels (based on E. coli activity) from continuous measurements at the springs. The combination of two water parameters was the most effective predictor at the two drinking water sources, which showed different optimal configuration of proxy parameters depending on their hydrogeological features and contaminant transport mechanisms. Gaussian Processes, Neural Networks, Naïve Bayes and Quadratic Discriminant Analysis provided the highest probability of correctly discriminating between sanitary risk levels. This methodology holds significant potential to be integrated as an early-warning protection tool for real-time sanitary risk assessment and, thus, safeguarding drinking water supplies worldwide against microbial threats.
PB ELSEVIER
YR 2026
FD 2026-02
LK https://hdl.handle.net/10630/41036
UL https://hdl.handle.net/10630/41036
LA eng
NO Fernández-Ortega, J., Barberá, J. A., & Andreo, B. (2026). New insights into machine learning prediction techniques for real-time sanitary risk assessment in karst drinking water sources affected by faecal contamination. Water Research, 290, 125060. https://doi.org/10.1016/j.watres.2025.125060
NO Disponible online desde el 29 de noviembre
NO Funding for open access charge: Universidad de Málaga / CBUA.
NO PRIMA funded European project KARMA (Karst Aquifer Resources availability and quality in the Mediterranean Area – ANR-18-PRIM-0005)
NO Spanish project, PCI2019–103675 of the International Combined Programme of the Ministry of Science, Innovation and Universities
NO Spanish project, PID2019–111759RB-I00  of the International Combined Programme of the Ministry of Science, Innovation and Universities
NO Research Group RNM-308 of the Junta de Andalucía
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026