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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kanevski, M., Timonin, V., & Pozdnoukhov, A. (2011). Automatic mapping and classification of spatial environmental data. Studies in Computational Intelligence, 348, 205–223. https://doi.org/10.1007/978-3-642-19733-8_12
Mendeley helps you to discover research relevant for your work.