Analysis of spatial hydrogeologic data using GEO3DSOM
نویسندگان
چکیده
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map L. Peeters, F. Bação, V. Lobo, and A. Dassargues Applied Geology and Mineralogy, KULeuven, Leuven, Belgium Instituto Superior de Estat́istica e Gestão de Informação, Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal Portuguese Naval Academy, Alfeite, Almada, Portugal Hydrogeology and Environmental Geology, University of Liege, Belgium Received: 17 May 2006 – Accepted: 5 June 2006 – Published: 11 July 2006 Correspondence to: L. Peeters ([email protected])
منابع مشابه
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map
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 5 knowledge into the algorithm. The performance of the ...
متن کاملInteractive comment on “Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map” by L. Peeters et al
The authors would like to thank the editor for the valuable contribution in the reviewing process of the submitted paper. The suggested minor editorial comments will be incorporated in the revised manuscript. With regards to the suggestion to stress the advantages and disadvantages of S1432
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