Automatic Mapping and Classification of Spatial Environmental Data
Data(s) |
2011
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Resumo |
The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories. |
Identificador |
http://serval.unil.ch/?id=serval:BIB_A28FA12CE344 doi:10.1007/978-3-642-19733-8_12 isbn:978-3-642-19733-8 |
Idioma(s) |
en |
Fonte |
Geocomputation, Sustainability and Environmental Planning, vol. 348, pp. 205-223 |
Palavras-Chave | #Automatic cartography; General Regression; Neural Networks; Probabilistic; Neural Networks; decision-oriented mapping; classification |
Tipo |
info:eu-repo/semantics/article article |