Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms

The Danube Delta, Europe's largest wetland, exhibits moderate ecological status based on biological indicators but shows good water quality when assessed through chemical parameters and machine-learning algorithms. This study leverages benthic fauna analysis alongside multicriterial indices to evaluate spatial and temporal variability in the Southern Branch of the Danube Delta (Saint Gheorghe). By integrating coliform bacteria data with chemical parameters, researchers developed a real-time classification model using machine learning, offering an innovative approach to water quality monitoring. The findings underscore the potential of computational methods to complement traditional ecological assessments, providing actionable insights for managing this critical UNESCO World Heritage Site under the Water Framework Directive.

Stoica, C., Camejo, J., Banciu, A., Nita-Lazar, M., Paun, I., Cristofor, S., Pacheco, O. R., & Guevara, M. (2016). Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms. Water Science and Technology, 73(10), 2413–2421. https://doi.org/10.2166/wst.2016.097