Geostatistical Estimation Techniques Applied to Radionuclide Deposition: an Accident Response Decision Aid

Charnock, T.W.; Daniels, W. M.; Higgins, N. A.

doi:10.1007/978-94-015-9297-0_32

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Site characterization is a critical step for the design of efficient remediation plans at contaminated sites. Geostatistics is increasingly used to model the spatial variability of contaminant concentrations and map them using generalized least-squares regression, known as kriging ( − ). Among many variants of kriging algorithms, ordinary kriging (OK) is the most straightforward and has been widely used ().…”

Section: Introductionmentioning

confidence: 99%

Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Saito

Goovaerts

2001

Environ. Sci. Technol.

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This paper presents a variant of the well-known kriging with a trend that allows one to account for the pollutant source coordinates and information about transport process into the spatial prediction of pollutant concentration. The new technique is illustrated using lead data from a Dallas metropolitan area and cadmium data from Palmerton (PA) NPL Superfund site. Instead of modeling the local spatial trend as low-order polynomials of coordinates, it is here expressed as a function of two factors that likely control the pollution spread: the distance to the smelter and the deviation from the major wind direction. Four different combinations of these two factors are developed, and their prediction performances are evaluated for a range of wind directions using cross-validation. Comparison with two traditional algorithms (OK and KT) shows that the proposed approach leads to smaller mean square errors of prediction when the correct wind direction is determined. The best combination of trend model and wind direction is site-dependent and derived using cross-validation. Kriging of residuals from a global trend model is an alternative to the use of local trends, and both techniques are shown to outperform ordinary kriging.

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Section: Introductionmentioning

confidence: 99%

Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Saito

Goovaerts

2001

Environ. Sci. Technol.

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Bayesian Calibration of Computer Models

Kennedy

O'Hagan

2001

Journal of the Royal Statistical Society Series B: Statistical Methodology

3,474

3,119

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Summary. We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of speci®c applications. However, in any speci®c application the values of necessary parameters may be unknown. In this case, physical observations of the system in the speci®c context are used to learn about the unknown parameters. The process of ®tting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc ®tting, and after calibration the model is used, with the ®tted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the ®tted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-®tting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise.

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Geostatistical Estimation Techniques Applied to Radionuclide Deposition: an Accident Response Decision Aid

Cited by 2 publications

References 3 publications

Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Bayesian Calibration of Computer Models

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