Modelling of ecological status of Polish lakes using deep learning techniques
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RESEARCH ARTICLE
Modelling of ecological status of Polish lakes using deep learning techniques Daniel Gebler 1
&
Agnieszka Kolada 2 & Agnieszka Pasztaleniec 2 & Krzysztof Szoszkiewicz 1
Received: 4 June 2020 / Accepted: 3 September 2020 # The Author(s) 2020
Abstract Since 2000, after the Water Framework Directive came into force, aquatic ecosystems’ bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered comprehensive hydrobiological databases. The amount of available data increases with each subsequent year of monitoring, and the efficient analysis of these data requires the use of proper mathematical tools. Our study challenges the comparison of the modelling potential between four indices for the ecological status assessment of lakes based on three groups of aquatic organisms, i.e. phytoplankton, phytobenthos and macrophytes. One of the deep learning techniques, artificial neural networks, has been used to predict values of four biological indices based on the limited set of the physicochemical parameters of water. All analyses were conducted separately for lakes with various stratification regimes as they function differently. The best modelling quality in terms of high values of coefficients of determination and low values of the normalised root mean square error was obtained for chlorophyll a followed by phytoplankton multimetric. A lower degree of fit was obtained in the networks for macrophyte index, and the poorest model quality was obtained for phytobenthos index. For all indices, modelling quality for non-stratified lakes was higher than this for stratified lakes, giving a higher percentage of variance explained by the networks and lower values of errors. Sensitivity analysis showed that among physicochemical parameters, water transparency (Secchi disk reading) exhibits the strongest relationship with the ecological status of lakes derived by phytoplankton and macrophytes. At the same time, all input variables indicated a negligible impact on phytobenthos index. In this way, different explanations of the relationship between biological and trophic variables were revealed. Keywords Artificial neural network . Biological indices . Macrophytes . Phytoplankton . Phytobenthos . Water quality
Introduction When the Water Framework Directive (WFD; Directive 2000/60/EC n.d.) came into force in 2000, lake assessment based on aquatic organisms has acquired immense practical importance. Based on the assumption that various ecosystem components, called biological quality elements (BQEs), are Responsible Editor: Marcus Schulz * Daniel Gebler [email protected] 1
Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland
2
Institute of Environmental Protection—National Research Institute, Kolektorska 4, 01-692 Warsaw, Poland
comprised of ecosystem status and reflect different aspects of its condition, the significant development
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