Automatic Decision-Oriented Mapping of Pollution Data
Details
Serval ID
serval:BIB_F4AEA8C1CEF6
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Automatic Decision-Oriented Mapping of Pollution Data
Title of the conference
Computational Science and Its Applications: International conference, Perugia, Italy, Proceedings, Part I
Publisher
Springer Berlin Heidelberg
ISBN
978-3-540-69839-5
ISSN-L
0302-9743
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Editor
Gervasi O., Murgante B., Laganà A., Taniar D., Mun Y., Gavrilova M.
Volume
5072
Pages
678-691
Language
english
Notes
Kanevski2008c
Abstract
The paper deals with the development and application of the methodology
for automatic mapping of pollution/contamination data. General Regression
Neural Network (GRNN) is considered in detail and is proposed as
an efficient tool to solve this problem. The automatic tuning of
isotropic and an anisotropic GRNN model using cross-validation procedure
is presented. Results are compared with k-nearest-neighbours interpolation
algorithm using independent validation data set. Quality of mapping
is controlled by the analysis of raw data and the residuals using
variography. Maps of probabilities of exceeding a given decision
level and ?thick? isoline visualization of the uncertainties are
presented as examples of decision-oriented mapping. Real case study
is based on mapping of radioactively contaminated territories.
for automatic mapping of pollution/contamination data. General Regression
Neural Network (GRNN) is considered in detail and is proposed as
an efficient tool to solve this problem. The automatic tuning of
isotropic and an anisotropic GRNN model using cross-validation procedure
is presented. Results are compared with k-nearest-neighbours interpolation
algorithm using independent validation data set. Quality of mapping
is controlled by the analysis of raw data and the residuals using
variography. Maps of probabilities of exceeding a given decision
level and ?thick? isoline visualization of the uncertainties are
presented as examples of decision-oriented mapping. Real case study
is based on mapping of radioactively contaminated territories.
Keywords
automatic cartography, General Regression Neural Networks, decision-oriented, mapping, uncertainty estimation
Create date
25/11/2013 18:18
Last modification date
20/08/2019 17:21
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