Machine Learning Models Forecast Pathogen Risks in Drinking Water

Researchers have deployed machine learning algorithms to predict pathogen spikes in drinking water sources, shifting water safety management from reactive testing to proactive risk mitigation. The approach uses routinely measured water‑quality indicators to forecast microbial contamination, allowing utilities to adjust treatment before pathogens reach municipal plants.

In a study led by Changzheng Cui at East China University of Science and Technology, 95 surface‑water samples from two major drinking‑water sources in Eastern China were analyzed. Six machine‑learning methods were compared; Random Forest and Decision‑Tree models achieved the strongest performance, with R² values above 0.75 and, for Pseudomonas aeruginosa, above 0.90. Independent samples collected in early 2026 confirmed the models' ability to predict beyond the training period. The findings suggest that integrating such predictive tools could help public‑health agencies protect vulnerable populations—infants, the elderly, and immunocompromised individuals—from water‑borne diseases such as Cryptosporidium, Giardia, and pathogenic Escherichia coli.