16.5 Conclusions
The current study has demonstrated that complex limnological time-series data can beneficially be processed by ANN in order to provide: (1) one-week-ahead forecasting of outbreaks of harmful algae or water quality changes by recurrent supervised ANN, and (2) clusters to unravel ecological relationships regarding seasons, water quality ranges and long-term environmental changes by non-supervised ANN. It has also been shown that these methods provide a useful framework for comparative studies between largely different lakes. Future work will focus on the integration of super- and non-supervised ANN into a representative lake data warehouse archiving long-term time-series of a broad range of lakes and rivers reflecting diverse climate, morphometric and eutrophic conditions. It will further facilitate “basic research on complex interactions (that) will lead to explanations for the variability and unpredictability that presently hamper lake management efforts...” Carpenter (1988).
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Recknagel, F., Welk, A., Kim, B., Takamura, N. (2006). Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_16
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