Machine Learning for Water Quality Assessment Based on Macrophyte Presence

The ecological state of the Danube River, as the world's most international river basin, will always be the focus of scientists in the field of ecology and environmental engineering. The concentration of orthophosphate anions in the river is one of the main indicators of the ecological state, i.e., water quality and level of eutrophication. The sedentary nature and ability to survive in river sections, combined with the presence of high levels of orthophosphate anions, make macrophytes an appropriate biological parameter for in situ prediction of in-river monitoring processes. However, a preliminary literature review identified a lack of comprehensive analysis that can enable the prediction of the ecological state of rivers using biological parameters as the input to machine learning (ML) techniques. This work focuses on comparing eight state-of-the-art ML classification models developed for this task. The data were collected at 68 sampling sites on both river sides. The predictive models use macrophyte presence scores as input variables, and classes of the ecological state of the Danube River based on orthophosphate anions, converted into a binary scale, as outputs. The results of the predictive model comparisons show that support vector machines and tree-based models provided the best prediction capabilities. They are also a low-cost and sustainable solution to assess the ecological state of the rivers.

Automated summary

The ecological state of the Danube River, as the most international river basin, is a major focus in the field of ecology and environmental engineering. The concentration of orthophosphate anions is an important indicator of water quality and eutrophication. This study compares eight state-of-the-art machine learning models to predict the river's ecological state using macrophyte presence scores as input variables. The results show that support vector machines and tree-based models provide the best prediction capabilities, offering a low-cost and sustainable solution for assessing river health.