English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The use of unsupervised artificial neural network techniques likethe self-organizing map (SOM) algorithm has proven to be a usefultool in exploratory data analysis and clustering of multivariatedata sets. In this study a variant of the SOM-algorithm is proposed,the GEO3DSOM, capable of explicitly incorporating three-dimensionalspatial knowledge into the algorithm. The performance of theGEO3DSOM is compared to the performance of the standard SOM inanalyzing an artificial data set and a hydrochemical data set. Thehydrochemical data set consists of 131 groundwater samples collectedin two detritic, phreatic, Cenozoic aquifers in Central Belgium.Both techniques succeed very well in providing more insight in thegroundwater quality data set, visualizing the relationships betweenvariables, highlighting the main differences between groups ofsamples and pointing out anomalous wells and well screens. TheGEO3DSOM however has the advantage to provide an increasedresolution while still maintaining a good generalization of the dataset

Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map