Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/27314
Citations | ||
Scopus | Web of ScienceĀ® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes |
Author: | Recknagel, F. Bobbin, J. Whigham, P. Wilson, H. |
Citation: | Journal of Hydroinformatics, 2002; 4(2):125-133 |
Publisher: | I W A Publishing |
Issue Date: | 2002 |
ISSN: | 1464-7141 1465-1734 |
Abstract: | <jats:p>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.</jats:p> |
DOI: | 10.2166/hydro.2002.0013 |
Published version: | http://dx.doi.org/10.2166/hydro.2002.0013 |
Appears in Collections: | Aurora harvest 2 Environment Institute publications Soil and Land Systems publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.