Reference : An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset:...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/4871
An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset: Application to Tanganyika Lake Dataset
English
Sorjamaa, Antti mailto [> >]
Lendasse, Amaury [> >]
Cornet, Yves mailto [Université de Liège - ULg > Département de géographie > Cartographie et systèmes d'information géographique >]
Deleersnijder, Eric [> >]
Jan-2010
Computational Geosciences
Springer Netherlands
14
1
55-64
Yes
International
1420-0597
1573-1499
Netherlands
[en] Missing Value Problem ; Empirical Orthogonal Functions - EOF ; Selection of Singular Values ; Tanganyika Lake
[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.
Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy
CLIMFISH
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/4871
10.1007/s10596-009-9132-3
© Springer Science + Business Media B.V. 2009

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