Dr Anna Budka

Dr Dariusz Kayzer

Prof. dr hab. inż. Krzysztof Szoszkiewicz

Karol Pietruczuk

Testing procedures for detection of observation influential for river assessment

Outliers are a common problem occurring in a set of environmental va-riables and such the observations often disturb significantly the final classification of the quality of aquatic ecosystems. Identification of outliers can reduce various sources of error in environmental monitoring, which can be caused by a person, equipment or method as well as other random factor. This paper examines how to detect outliers by using some statistical tests. Four tests were analysed: Q-Dixon, Grubbs, Hampel and quartiled. All of them were evaluated in terms of detection sensitivity of typical outliers. The analytical dataset consisted of a monthly analysis of the various forms of nitrogen and phosphorus in the water. The study was conducted Turing the single year (2010) in two rivers in Wielkopolska region (Głomia and Mogielnica) representing different degrees of degradation. ...

Mgr inż. Daniel Gebler

Dr Dariusz Kayzer

Dr Anna Budka

Prof. dr hab. inż. Krzysztof Szoszkiewicz

Modelling values of river macrophyte metrics using artificial neural networks

The results of field research at 230 river sections located throughout Poland were used to examine the possibility of predicting values of macrophyte metrics of ecological status. Artificial intelligence methods such as artificial neural networks were used in the modelling. The physicochemical parameters of water (alkalinity, conductivity, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand) were used as the explanatory (modelling) variables. The explained (modelled) parameters were the Polish MIR (Macrophyte Index for Rivers), the British MTR (Mean Trophic Rank) and the French IBMR (River Macrophytes Biological Index). The quality of the constructed models was assessed using the normalized root mean square error (NRMSE) and the r-Pearson's linear correlation coefficient between variables modelled by the networks and calculated on the basis of the botanical research. These analyses demonstrated that the network modelling MIR values had the highest accuracy. The lowest prediction accuracy was obtained for MTR and IBMR indices. The differences between particular models are likely to result from better adjustment of the Polish method to local rivers (particularly in terms of indicator species used). ...