Data/hora: 14/06/2024 16:22:44

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Territorial.	
Data corrente: 	14/01/2019	
Data da última atualização: 	10/12/2020	
Autoria: 	SILVA, M. A. S. da; MACIEL, R. J. S.; MATOS, L. N.; DOMPIERI, M. H. G.	
Afiliação: 	MARCOS AURELIO SANTOS DA SILVA, CPATC; RENATO JOSE SANTOS MACIEL, CNPTIA; LEONARDO N. MATOS, UNIVERSIDADE FEDERAL DO SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM.	
Título: 	Automatic environmental zoning with self-organizing maps.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	Modern Environmental Science and Engineering, v. 4, n. 9, p. 872-881, Sept. 2018.	
ISBN: 	2333-2581	
DOI: 	10.15341/mese(2333-2581)/09.04.2018/011	
Idioma: 	Inglês
Português	
Conteúdo: 	This 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.	
Palavras-Chave: 	Alto Taquari river; Alto Taquari river basin; Análise de correspondência; Artificial neural network; Exploratory spatial analysis; Redes neurais artificiais; Similarity coefficients.	
Thesaurus Nal: 	Correspondence analysis; Neural networks.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/190432/1/5030.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPM - (5030-1) - UPC - AP - DD - 18/078 - AP - 2018.078 - DIS
CNPTIA - (20828-1) - UPC - AP - DD -  -  -  - DIS