Radial basis functions versus geostatistics in spatial interpolations

A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular meth...

Descripción completa

Detalles Bibliográficos
Autores principales: Rusu, Cristian, Rusu, Virginia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
spatial interpolation
environmental monitoring
Radial Basis Functions (RBF)
Computing Methodologies
Neural nets
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23875
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23875
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
spatial interpolation
environmental monitoring
Radial Basis Functions (RBF)
Computing Methodologies
Neural nets
spellingShingle Ciencias Informáticas
spatial interpolation
environmental monitoring
Radial Basis Functions (RBF)
Computing Methodologies
Neural nets
Rusu, Cristian
Rusu, Virginia
Radial basis functions versus geostatistics in spatial interpolations
topic_facet Ciencias Informáticas
spatial interpolation
environmental monitoring
Radial Basis Functions (RBF)
Computing Methodologies
Neural nets
description A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and the correct interpretation of the interpolation results are key elements of the spatial interpolation process. A powerful alternative to geostatistics in spatial interpolation is the use of the soft computing methods. They offer the potential for a more flexible, less assumption dependent approach. Artificial Neural Networks are well suited for this kind of problems, due to their ability to handle non-linear, noisy, and inconsistent data. The present paper intends to prove the advantage of using Radial Basis Functions (RBF) instead of geostatistics in spatial interpolations, based on a detailed analyze and modeling of the SIC2004 (Spatial Interpolation Comparison) dataset.
format Objeto de conferencia
Objeto de conferencia
author Rusu, Cristian
Rusu, Virginia
author_facet Rusu, Cristian
Rusu, Virginia
author_sort Rusu, Cristian
title Radial basis functions versus geostatistics in spatial interpolations
title_short Radial basis functions versus geostatistics in spatial interpolations
title_full Radial basis functions versus geostatistics in spatial interpolations
title_fullStr Radial basis functions versus geostatistics in spatial interpolations
title_full_unstemmed Radial basis functions versus geostatistics in spatial interpolations
title_sort radial basis functions versus geostatistics in spatial interpolations
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23875
work_keys_str_mv AT rusucristian radialbasisfunctionsversusgeostatisticsinspatialinterpolations
AT rusuvirginia radialbasisfunctionsversusgeostatisticsinspatialinterpolations
bdutipo_str Repositorios
_version_ 1764820466363531264

Ejemplares similares