Spatiotemporal modelling of groundwater contaminants

Molinari, Daniel Alberto (2014) Spatiotemporal modelling of groundwater contaminants. PhD thesis, University of Glasgow.

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Spatiotemporal data have become very common, particularly through environmental settings where a spatial array of sampling sites generates data over time. This thesis deals with a specific spatio-temporal setting of groundwater contamination and aims to construct suitable statistical models. One of the motivating features of the application is that the model has to be implemented in an unsupervised manner and there is a high premium on the results being available very quickly, with a response time of a few seconds only. Many routes to spatiotemporal models are possible, but in order to achieve the aims outlined above we have proposed a model based on P-splines. A Bayesian approach to fitting is used to provide the stability required in an unsupervised setting. The speed requirement makes computationally intensive methods such as MCMC unsuitable for the determination of the optimal penalisation parameter and so conjugate priors and highly efficient methods of linear algebra have been brought to bear. Use of the model identified a problematic issue due to the irregular spatio-temporal design of some data sets, giving rise to cases of \ballooning", where unexpectedly high predictions, not supported by the observations, can appear. This matter was also tackled within the Bayesian framework mentioned above. The proposed procedures were assessed both by means of a simulation study and on real data. Finally, as an extension of the proposed methodology, we address the issue of non-detects, namely observations which are known only to lie below some limit of detection. The task is accomplished using a Laplace-type approximation to the posterior distribution of the parameters and the suitability of this approximation is analysed through examples. The problems addressed in the thesis are motivated by the need to ensure environmental quality in and around installations operated by the multinational company Shell. The assistance of Shell in advising on the context of the issues, and in providing data sets for case studies, is much appreciated.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: spatiotemporal, groundwater, contaminants
Subjects: Q Science > QA Mathematics
Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
Funder's Name: UNSPECIFIED
Supervisor's Name: Bowman, Professor Adrian and Evers, Doctor Ludger
Date of Award: 2014
Depositing User: Mr Daniel Alberto Molinari
Unique ID: glathesis:2014-5873
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 20 Jan 2015 15:06
Last Modified: 21 Jan 2015 09:34

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