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| Titel |
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map |
| VerfasserIn |
L. Peeters, F. Bação, V. Lobo, A. Dassargues |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 11, no. 4 ; Nr. 11, no. 4 (2007-05-11), S.1309-1321 |
| Datensatznummer |
250009387
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| Publikation (Nr.) |
 copernicus.org/hess-11-1309-2007.pdf |
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| Zusammenfassung |
| The use of unsupervised artificial neural network techniques like
the self-organizing map (SOM) algorithm has proven to be a useful
tool in exploratory data analysis and clustering of multivariate
data sets. In this study a variant of the SOM-algorithm is proposed,
the GEO3DSOM, capable of explicitly incorporating three-dimensional
spatial knowledge into the algorithm. The performance of the
GEO3DSOM is compared to the performance of the standard SOM in
analyzing an artificial data set and a hydrochemical data set. The
hydrochemical data set consists of 131 groundwater samples collected
in two detritic, phreatic, Cenozoic aquifers in Central Belgium.
Both techniques succeed very well in providing more insight in the
groundwater quality data set, visualizing the relationships between
variables, highlighting the main differences between groups of
samples and pointing out anomalous wells and well screens. The
GEO3DSOM however has the advantage to provide an increased
resolution while still maintaining a good generalization of the data
set. |
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