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| Reference : An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset: Ap... |
| 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  [> >] | |
| Lendasse, Amaury [> >] | |
| Cornet, Yves [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 | |
| 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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