A study published on 10 October in Scientific Reports developed a machine-learning framework to examine industrial corrosion and health risks using 45 groundwater samples from Algeria’s Souf Valley. The authors compared an artificial neural network, random forest and decision tree to estimate corrosion and scaling potential indices and a water-quality index. The paper reports test R² values above 0.90 for six of the eight indices. Author affiliations also include institutions in Egypt, the United Arab Emirates and Saudi Arabia.

The researchers classified 60% of the samples as corrosive and 85% as having high corrosion risk; 10,000 Monte Carlo iterations for nitrate exposure produced a mean child hazard index above 1. These findings come from a small sample in one aquifer and reflect the hydrochemical conditions studied for 2020; they are not validated general predictions for other regions or seasons. The paper does not present the model as a monitoring system that replaces field measurements.