BME-based uncertainty assessment of the Chernobyl fallout

E. Savelieva, V. Demyanov, M. Kanevski, M. Serre, G. Christakos

    Research output: Contribution to journalArticle

    Abstract

    The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements ("hard" data) are combined with secondary information on local uncertainties (treated as "soft" data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study. © 2005 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)312-324
    Number of pages13
    JournalGeoderma
    Volume128
    Issue number3-4 SPEC. ISS.
    DOIs
    Publication statusPublished - Oct 2005

    Fingerprint

    fallout
    entropy
    uncertainty analysis
    Chernobyl accident
    kriging
    risk assessment
    soil
    method
    distribution
    prediction

    Keywords

    • Bayesian maximum entropy
    • Probability distribution
    • Radioactive contamination
    • Uncertainty

    Cite this

    Savelieva, E., Demyanov, V., Kanevski, M., Serre, M., & Christakos, G. (2005). BME-based uncertainty assessment of the Chernobyl fallout. Geoderma, 128(3-4 SPEC. ISS.), 312-324. https://doi.org/10.1016/j.geoderma.2005.04.011
    Savelieva, E. ; Demyanov, V. ; Kanevski, M. ; Serre, M. ; Christakos, G. / BME-based uncertainty assessment of the Chernobyl fallout. In: Geoderma. 2005 ; Vol. 128, No. 3-4 SPEC. ISS. pp. 312-324.
    @article{6a5dff779f2e4e8aa74a41193afabd4c,
    title = "BME-based uncertainty assessment of the Chernobyl fallout",
    abstract = "The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements ({"}hard{"} data) are combined with secondary information on local uncertainties (treated as {"}soft{"} data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study. {\circledC} 2005 Elsevier B.V. All rights reserved.",
    keywords = "Bayesian maximum entropy, Probability distribution, Radioactive contamination, Uncertainty",
    author = "E. Savelieva and V. Demyanov and M. Kanevski and M. Serre and G. Christakos",
    year = "2005",
    month = "10",
    doi = "10.1016/j.geoderma.2005.04.011",
    language = "English",
    volume = "128",
    pages = "312--324",
    journal = "Geoderma",
    issn = "0016-7061",
    publisher = "Elsevier",
    number = "3-4 SPEC. ISS.",

    }

    Savelieva, E, Demyanov, V, Kanevski, M, Serre, M & Christakos, G 2005, 'BME-based uncertainty assessment of the Chernobyl fallout', Geoderma, vol. 128, no. 3-4 SPEC. ISS., pp. 312-324. https://doi.org/10.1016/j.geoderma.2005.04.011

    BME-based uncertainty assessment of the Chernobyl fallout. / Savelieva, E.; Demyanov, V.; Kanevski, M.; Serre, M.; Christakos, G.

    In: Geoderma, Vol. 128, No. 3-4 SPEC. ISS., 10.2005, p. 312-324.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - BME-based uncertainty assessment of the Chernobyl fallout

    AU - Savelieva, E.

    AU - Demyanov, V.

    AU - Kanevski, M.

    AU - Serre, M.

    AU - Christakos, G.

    PY - 2005/10

    Y1 - 2005/10

    N2 - The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements ("hard" data) are combined with secondary information on local uncertainties (treated as "soft" data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study. © 2005 Elsevier B.V. All rights reserved.

    AB - The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements ("hard" data) are combined with secondary information on local uncertainties (treated as "soft" data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study. © 2005 Elsevier B.V. All rights reserved.

    KW - Bayesian maximum entropy

    KW - Probability distribution

    KW - Radioactive contamination

    KW - Uncertainty

    UR - http://www.scopus.com/inward/record.url?scp=23744493095&partnerID=8YFLogxK

    U2 - 10.1016/j.geoderma.2005.04.011

    DO - 10.1016/j.geoderma.2005.04.011

    M3 - Article

    VL - 128

    SP - 312

    EP - 324

    JO - Geoderma

    JF - Geoderma

    SN - 0016-7061

    IS - 3-4 SPEC. ISS.

    ER -

    Savelieva E, Demyanov V, Kanevski M, Serre M, Christakos G. BME-based uncertainty assessment of the Chernobyl fallout. Geoderma. 2005 Oct;128(3-4 SPEC. ISS.):312-324. https://doi.org/10.1016/j.geoderma.2005.04.011