Missing Value Problem; Empirical Orthogonal Functions - EOF; Selection of Singular Values; Tanganyika Lake
Abstract :
[en] In this paper, an improved methodology for the determination of
missing values in a spatio-temporal database is presented. This methodology
performs denoising projection in order to accurately fill the missing values
in the database. The improved methodology is called EOF Pruning and it
is based on an original linear projection method called Empirical Orthogo-
nal Functions (EOF). The experiments demonstrate the performance of the
improved methodology and present a comparison with the original EOF and
with a widely-used Optimal Interpolation method called Objective Analysis.
Disciplines :
Computer science
Author, co-author :
Sorjamaa, Antti
Lendasse, Amaury
Cornet, Yves ; Université de Liège - ULiège > Département de géographie > Cartographie et systèmes d'information géographique
Deleersnijder, Eric
Language :
English
Title :
An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset: Application to Tanganyika Lake Dataset
Publication date :
January 2010
Journal title :
Computational Geosciences
ISSN :
1420-0597
eISSN :
1573-1499
Publisher :
Springer Netherlands, Netherlands
Volume :
14
Issue :
1
Pages :
55-64
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
CLIMFISH
Funders :
BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
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