o:1605 Machine Learning for Water Quality Assessment Based on Macrophyte Presence en Abstract: 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. 1552099 10.3390/su15010522 1552101 2071-1050 2023-04-10T08:16:14.939Z 44 yes 46 Ivana Krtolica 0000-0002-1816-253X Dragan Savić person 0000-0001-9567-9041 Bojana Bajić person 0000-0001-7843-7091 Snežana Radulović person application/pdf 824387 https://unilib.phaidrabg.rs/o:1605 no yes 16 70 92000001 71A08 Sustainability 15 1 522 2023