Redundancy analysis and the evolutionary learning algorithm as complementary processing tools for dendrochronological data
- H. Beeckman
- K. Vander Mijnsbrugge
Abstract
To process dendrochronological data sets, mathematical techniques that can handle complexity are needed. Two methods from the field of numerial ecology are introduced in tree ring analysis: redundancy analysis (an eigenvector method) and the evolutionary learning algorithm (a machine learning tool). Both methods show to be appropriate for a stringent test case. Redundancy analysis explains variance in tree ring data by environmental date revealing main trends. The evolutionary learning algorithm can be applied to look for unexpected strong environmental signals possibly departing from main trends.
How to Cite:
Beeckman, H. & Vander Mijnsbrugge, K., (1993) “Redundancy analysis and the evolutionary learning algorithm as complementary processing tools for dendrochronological data”, Silva Gandavensis 58. doi: https://doi.org/10.21825/sg.v58i0.881
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