Patternising phytoplankton dynamics of two shallow lakes in response to restoration measures by applying non-supervised artificial neural networks

A. Talib (), F. Recknagel and D. Molen
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A. Talib: University of Adelaide
F. Recknagel: University of Adelaide
D. Molen: Institute of Inland Water Management

Environment Systems and Decisions, 2007, vol. 27, issue 1, 195-205

Abstract: Abstract Long-term time-series data sets of two shallow Dutch lakes, Lake Veluwemeer and Lake Wolderwijd were subjected to ordination and clustering by means of non-supervised artificial neural networks (ANN). Splitting of the data sets into sub-series corresponding with three different management periods have allowed a comparative analysis of both the short-term seasonal and long-term phytoplankton dynamics in relation to the restoration measures. The lakes were considered as hyper-eutrophic and have been managed both with bottom-up and top-down management approaches. Results of the study have demonstrated that non-supervised ANN allow to elucidate causal relationships of complex ecological processes (1) within the specific genus, Oscillatoria and Scenedesmus and (2) the combination of external nutrient control and in-lake food web manipulation of the two lakes achieved to control eutrophication.

Keywords: Bottom-up management; Eutrophication; Non-supervised ANN; Oscillatoria; Scenedesmus; Shallow lakes; Top-down management (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s10669-007-9023-x

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