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A machine learning approach for characterizing soil contamination in the presence of physical site discontinuities and aggregated samples

Alyssa Ngu-Oanh Quach, Lucie Tabor, Dany Dumont, Benoit Courcelles and James-A. Goulet

Article (2017)

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Cite this document: Quach, A. N.-O., Tabor, L., Dumont, D., Courcelles, B. & Goulet, J.-A. (2017). A machine learning approach for characterizing soil contamination in the presence of physical site discontinuities and aggregated samples. Advanced Engineering Informatics, 33, p. 60-67. doi:10.1016/j.aei.2017.05.002
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Abstract

Rehabilitation of contaminated soils in urban areas is in high demand because of the appreciation of land value associated with the increased urbanization. Moreover, there are financial incentives to minimize soil characterization uncertainties. Minimizing uncertainty is achieved by providing models that are better representation of the true site characteristics. In this paper, we propose two new probabilistic formulations compatible with Gaussian Process Regression (GPR) and enabling (1) to model the experimental conditions where contaminant concentration is quantified from aggregated soil samples and (2) to model the effect of physical site discontinuities. The performance of approaches proposed in this paper are compared using a Leave One Out Cross-Validation procedure (LOO-CV). Results indicate that the two new probabilistic formulations proposed outperform the standard Gaussian Process Regression.

Open Access document in PolyPublie
Subjects: 1000 Génie civil > 1000 Génie civil
Department: Département des génies civil, géologique et des mines
Research Center: Non applicable
Funders: Fonds de recherche du Québec - Nature et technologies (FRQNT)
Grant number: 2017-NC-197235
Date Deposited: 15 Jan 2018 13:55
Last Modified: 18 May 2019 01:15
PolyPublie URL: https://publications.polymtl.ca/2843/
Document issued by the official publisher
Journal Title: Advanced Engineering Informatics (vol. 33)
Publisher: Elsevier
Official URL: https://doi.org/10.1016/j.aei.2017.05.002

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