A fuzzy interval analysis approach to kriging with ill-known variogram and data
| Autor: | Didier Dubois, Kevin Loquin |
|---|---|
| Přispěvatelé: | Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Queen's University [Belfast] (QUB), ANR-06-PCO2-0003,CRISCO2,Critères de sécurité pour le stockage du CO2 : approche qualitative / quantitative de scénarios de risques(2006) |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Optimization
Epistemic uncertainty 02 engineering and technology Geostatistics 010502 geochemistry & geophysics computer.software_genre 01 natural sciences Theoretical Computer Science [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Kriging Statistics 0202 electrical engineering electronic engineering information engineering Variogram Uncertainty quantification 0105 earth and related environmental sciences Mathematics Possibility theory Random function Statistical model 020201 artificial intelligence & image processing Spatial variability Geometry and Topology Data mining Fuzzy subset computer Software |
| Zdroj: | Soft Computing Soft Computing, Springer Verlag, 2012, 16 (5), pp.769-784. ⟨10.1007/s00500-011-0768-2⟩ |
| ISSN: | 1432-7643 1433-7479 |
| Popis: | International audience; Geostatistics is a branch of statistics dealing with spatial phenomena. Kriging consists in estimating or predicting a spatial phenomenon at non-sampled locations from an estimated random function. It is assumed that, under some well-chosen simplifying hypotheses of stationarity, the probabilistic model, i.e. the random function describing spatial variability dependencies, can be completely assessed from the dataset. However, in the usual kriging approach, the choice of the random function is mostly made at a glance by the experts (i.e. geostatisticians), via the selection of a variogram from a thorough descriptive analysis of the dataset. Although information necessary to properly select a unique random function model seems to be partially lacking, geostatistics, in general, and the kriging methodology, in particular, does not account for the incompleteness of the information that seems to pervade the procedure. The paper proposes an approach to handle epistemic uncertainty appearing in the kriging methodology. On the one hand, the collected data may be tainted with errors that can be modelled by intervals or fuzzy intervals. On the other hand, the choice of parameter values for the theoretical variogram, an essential step, contains some degrees of freedom that are seldom acknowledged. In this paper, we propose to account for epistemic uncertainty pervading the variogram parameters, and possibly the dataset, and lay bare its impact on the kriging results, improving on previous attempts by Bardossy and colleagues in the late 1980s. |
| Databáze: | OpenAIRE |
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