01797naa a2200289 a 450000100080000000500110000800800410001902200140006002400490007410000230012324500850014626000090023152008660024065000280110665000200113465300230115465300290117765300330120665300300123965300330126965300300130265300280133270000210136070000170138170000230139877300860142121279612020-12-10       2018    bl uuuu     u00u1 u    #d  a2333-25817 a10.15341/mese(2333-2581)/09.04.2018/0112DOI1 aSILVA, M. A. S. da  aAutomatic environmental zoning with self-organizing maps.h[electronic resource]  c2018  aThis article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic environmental zoning by combining the handle of categorical data and the other for automatic clustering. The SOM online learning algorithm had been chosen to treat categorical data by using the dot product method and the Sorense-Dice binary similarity coefficient. To automatically perform a spatial clustering, an adaptation of the automatic clustering Costa-Netto algorithm had been also proposed. The correspondence analysis had been used to examine the profiles of each homogeneous zones. To explore the approach it has been performed the environmental zoning of the Alto Taquari River Basin, Brazil, using as input data a set of thematic maps. The results indicate the applicability of the approach to perform the exploratory environmental zoning.  aCorrespondence analysis  aNeural networks  aAlto Taquari river  aAlto Taquari river basin  aAnálise de correspondência  aArtificial neural network  aExploratory spatial analysis  aRedes neurais artificiais  aSimilarity coefficients1 aMACIEL, R. J. S.1 aMATOS, L. N.1 aDOMPIERI, M. H. G.  tModern Environmental Science and Engineeringgv. 4, n. 9, p. 872-881, Sept. 2018.