Zusammenfassung
The paper compares potentials and achievements of
artificial neural networks and genetic algorithms in
terms of forecasting and understanding of algal blooms
in Lake Kasumigaura (Japan). Despite the complex and
nonlinear nature of ecological data, artificial neural
networks allow seven-days-ahead predictions of timing
and magnitudes of algal blooms with reasonable
accuracy. Genetic algorithms possess the capability to
evolve, refine and hybridize numerical and linguistic
models. Examples presented in the paper show that
models explicitly synthesized by genetic algorithms not
only perform better in seven-days-ahead predictions of
algal blooms than artificial neural network models, but
provide more transparency for explanation as well.
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