Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME

Modis, K; Vatalis, K; Papantonopoulos, G; Sachanidis, C

dc.contributor.author	Modis, K	en
dc.contributor.author	Vatalis, K	en
dc.contributor.author	Papantonopoulos, G	en
dc.contributor.author	Sachanidis, C	en
dc.date.accessioned	2014-03-01T01:34:48Z
dc.date.available	2014-03-01T01:34:48Z
dc.date.issued	2010	en
dc.identifier.issn	1436-3240	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20871
dc.subject	BME	en
dc.subject	Geostatistics	en
dc.subject	Risk assessment	en
dc.subject	Uncertain measurements	en
dc.subject	Water pollution	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Statistics & Probability	en
dc.subject.classification	Water Resources	en
dc.subject.other	Bayesian maximum entropies	en
dc.subject.other	BME	en
dc.subject.other	Chemical pollutants	en
dc.subject.other	Conventional measurements	en
dc.subject.other	Data sets	en
dc.subject.other	Essential component	en
dc.subject.other	Geo-statistics	en
dc.subject.other	Hydrogeological	en
dc.subject.other	Interval data	en
dc.subject.other	Irregular sampling	en
dc.subject.other	Life span	en
dc.subject.other	Manganese concentration	en
dc.subject.other	New York	en
dc.subject.other	Open-cast mining	en
dc.subject.other	Oxford University	en
dc.subject.other	Posterior distributions	en
dc.subject.other	Seasonal variation	en
dc.subject.other	Single point	en
dc.subject.other	Spatial analysis	en
dc.subject.other	Spatio-temporal processing	en
dc.subject.other	Spatiotemporal distributions	en
dc.subject.other	Temporal trends	en
dc.subject.other	Uncertain measurements	en
dc.subject.other	Uncertainty management	en
dc.subject.other	Uncertainty sources	en
dc.subject.other	Water contamination	en
dc.subject.other	Ammonium compounds	en
dc.subject.other	Boreholes	en
dc.subject.other	Data processing	en
dc.subject.other	Groundwater	en
dc.subject.other	Groundwater pollution	en
dc.subject.other	Lignite	en
dc.subject.other	Manganese	en
dc.subject.other	Oil spills	en
dc.subject.other	Risk assessment	en
dc.subject.other	Risk management	en
dc.subject.other	Surface topography	en
dc.subject.other	Uncertainty analysis	en
dc.subject.other	data processing	en
dc.subject.other	geostatistics	en
dc.subject.other	hydrogeology	en
dc.subject.other	karst	en
dc.subject.other	lignite	en
dc.subject.other	maximum entropy analysis	en
dc.subject.other	opencast mining	en
dc.subject.other	risk assessment	en
dc.subject.other	seasonal variation	en
dc.subject.other	spatial analysis	en
dc.subject.other	underground storage	en
dc.subject.other	water pollution	en
dc.subject.other	Greece	en
dc.title	Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00477-008-0298-3	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00477-008-0298-3	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	The occurrence of chemical pollutants in ground water is an issue of considerable interest. In the case of Ptolemais lignite opencast mining area in Greece, ammonium, nitrites, nitrates, iron total and total manganese concentrations, as well as various other elements have been monitored since the early 2000s through a borehole network. The continuous, though, alteration of the surface topography due to the intensive mining works, limits the life span of the water boreholes and results to irregular spatiotemporal distribution of the samples. Regarding the problem of mapping the water contamination, the coarse and irregular sampling pattern, combined with absence of seasonal variations and temporal trends, does not facilitate spatiotemporal processing of the data. On the other hand, a mere spatial analysis requires the attribution of the whole set of monitored values for each borehole, to a single point in space. The objective of this work is to develop a methodology to cope with the problem of uncertainty caused due to the above assumptions. The proposed solution is based on the consideration of interval data. The Bayesian maximum entropy (BME) theory (Christakos, Modern spatiotemporal geostatistics. Oxford University Press, New York, 2000) is an essential component of this methodology. The main reason for this is that it is the only theory in the framework of Geostatistics that offers powerful tools to merge the uncertainty sources with the rest of conventional measurements. This ability is a result of its generalized view on the problem of estimation that focuses on the knowledge of a natural variable and not the variable itself. The application of the proposed methodology led to sharper posterior distributions, an indication of the increased certainty in estimated values which is induced by the broader utilization of data. © Springer-Verlag 2008.	en
heal.publisher	SPRINGER	en
heal.journalName	Stochastic Environmental Research and Risk Assessment	en
dc.identifier.doi	10.1007/s00477-008-0298-3	en
dc.identifier.isi	ISI:000273329300005	en
dc.identifier.volume	24	en
dc.identifier.issue	1	en
dc.identifier.spage	47	en
dc.identifier.epage	56	en