- author = "Friedrich Recknagel and Jason Bobbin and Peter A. Whigham and Hugh Wilson",
- title = "Comparative Application of Artificial Neural Networks and Genetic Algorithms for Multivariate Time Series Modelling of Algal Blooms in Freshwater Lakes",
- journal = "Journal of Hydroinformatics",
- year = "2002",
- volume = "4",
- number = "2",
- pages = "125--133",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Ecology, chlorophyll-a, Microcystis, short-term prediction, artificial neural network model, genetic algorithm model, rule sets, difference equations",
- ISSN = "1464-7141",
-
URL = "
http://jh.iwaponline.com/content/4/2/125",
-
URL = "
http://www.iwaponline.com/jh/004/0125/0040125.pdf",
-
DOI = "
doi:10.2166/hydro.2002.0013",
- size = "9 pages",
- abstract = "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.",
-
notes = "More GA than GP? GAMA
Department of Soil and Water, University of Adelaide, Glen Osmond, SA 5064, Australia
Department of Computer Science, University of Otago, PO Box 56, Dunedin, New Zealand",
