2026-05-13T16:13:12.866Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:8117 2021-04-27T15:44:06Z 2

Water quality and macrophytes in the Danube River: Artificial neural network modelling 2021 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/8117 https://www.sciencedirect.com/science/article/pii/S1470160X20310153 I. Krtolica D. Cvijanović Đ. Obradović M. Novković Đ. Milošević D. Savić M. Vojinović Miloradov S. Radulović Ecological assessment of large rivers such as the Danube is a challenging task. Eutrophication was reported as one of the main drivers that structure aquatic communities in the Danube basin. Due to their sedentary nature, relatively slow growth/ long life spans, and engineering role in aquatic ecosystems, macrophytes are widely used in the detection of nutrient enrichment. In this study, macrophyte presence-absence data within the 3 km long reaches obtained from the Joint Danube Survey (JDS3) were used to predict the water quality of the Danube river and its main tributaries. For each water quality variable (dissolved oxygen, nitrate-nitrogen, and orthophosphates), a multi-layer feed-forward artificial neural network model (ANN) was constructed using the macrophytes as explanatory variables. Despite the limited number of samples (123) along the wide trophic gradient of the Danube, the model showed good article https://doi.org/10.1016/j.ecolind.2020.107076 121 1 20 1470-160X Ecological Indicators