Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

The study focused on modelling of macropyte indices against physico-chemical parameters of waters by artificial neural networks. Several macrophyte diversity indices were analysed (species richness— N , the Shannon index— H ′, the Simpson index— D , and the Pielou index— J ) as well as the ecological status index (the Macrophyte Index for Rivers—MIR). The aim of the study was to verify knowledge about potential application of macrophytes in the environmental monitoring. A Multi-Layer Perceptron type of network was used in the analyses. The study included 260 river sites located throughout Poland. Alkalinity, conductivity, pH, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand were used as the explanatory variables. The quality of the constructed models was assessed using calculated errors (RMSE and NRMSE) and r Pearson’s linear correlation coefficient. The neural network for the MIR index was characterised by the highest quality. Neural networks for other diversity indices ( N , H ′, D , and J ) did not provide adequate results for modelling, which shows their ineffectiveness biological monitoring. Sensitivity analysis revealed the influence of each variable to the models. It indicated that modelled values of MIR are most strongly influenced by total phosphorus and alkalinity.