A BME solution of the inverse problem for saturated groundwater flow

Serre, Marc L.; Christakos, George; Li, H.; Miller, Cass T.

doi:10.1007/s00477-003-0156-2

Cited by 30 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This methodology has already been applied successfully to ground water modeling problems (Christakos 1998;Serre et al 2003a). In order to ensure the proper calculation of the covariance function, which will be the main structural characteristic of the general knowledge base G as stated previously, it is necessary to work on a homogeneous RF.…”

Section: The Prior Stagementioning

confidence: 99%

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

Modis

Vatalis²,

Papantonopoulos

et al. 2009

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

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.

show abstract

Section: The Prior Stagementioning

confidence: 99%

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

Modis

Vatalis²,

Papantonopoulos

et al. 2009

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…It can therefore cope with non-Gaussian conditions. BME has been used in applications dealing with urban sustainability (Brazel et al 2007), climatology , hydrology (Serre et al 2003a;Lee and Wentz 2008), exposure and health mapping (Lee 2005;Akita et al 2007;Puangthongthub et al 2007), risk assessment (Serre et al 2003b;Choi et al 2007), and geographical epidemiology (Law et al 2004). These applications show that BME is a promising estimator, but there are, to our knowledge, no studies that use BME to forecast the distribution of a variable in space and time.…”

Section: Introductionmentioning

confidence: 96%

Space–time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona

Lee

Wentz

Gober

2009

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions. We use space-time variability in historical data and projections of future population density to improve forecasting of residential water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical moments between a dependent variable, water use, and an independent variable, population density. The independent variable is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating the uncertain estimates into the space-time forecasting process improves forecasting accuracy up to 43.9% over other space-time mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the future, provides up-to-date forecasts of water use, and can be adapted to other socio-economic and environmental applications.

show abstract

“…A large number of papers have been reported dealing with the variability in space of environmental properties which in turn influence predictions of pesticide leaching models, such as chemical (Wood et al 1987), hydraulical (Bosch and West 1998), biological (Soulas and Lagacherie 2001), and hydro-geological data (Singh 1997;Krajewski et al 1998;Chaubey et al 1999;Wingle et al 1999;Nolan et al 2003;Triantafilis et al 2003). Stochastic modeling of uncertainty exploit in random spatial data, is often preferred (Tarboton et al 1995;Carlson and Osiensky 1998;Perreault et al 2000;Prahash and Singh 2000;Paralta and Ribeiro 2001;Sa´n-chez-Martos et al 2001;Gaus et al 2003;Serre et al 2003;Verma et al 2003). This models are of great help in studying natural hazards and risk assessment, especially for estimating geophysical processes in regions where no monitoring stations are present (Salvadori et al 2001).…”

Section: Introductionmentioning

confidence: 96%

Environinformatics in ecological risk assessment of agroecosystems pollutant leaching

Diodato¹,

Ceccarelli

2005

Stoch Environ Res Ris Assess

View full text Add to dashboard Cite

In this paper, a novel approach for mapping leaching risk at large sub-regional scale under limited information is presented, with acronym environinformatics in ecological risk assessment. The problem consists into quantifying the exchange frequency of the plant available soil water (Narula et al. in J Geogr Inform Decision Anal 7(1) [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] 2002), this frequency can be adopted as a measure indicating the nutrient and contaminant leaching risk for a site. Our approach is based on integrating soil water balance with spatial analysis tools. However, any decision involved in scientific risk evaluation requires the accurate quantification of the degree of uncertainty arising from sampling, modelling and interpolation errors. The non-parametric geostatistical procedure of Indicator Kriging enables to circumvent this problem by estimating the probability that the true value exceed a set of threshold values. The transformation of leaching data to a binary response variable, known as ''indicator'', can lead to a soft description of leaching. Such soft description can mitigate the uncertainty in exchange frequency estimates of the plant available soil water. The approach was applied to a test site in Beneventan agroecosystem (South Italy) by using a long-term hydrological water balance acquired in a 40-years period. In this way, about 400 km 2 (25%) of the total 2,000 km 2 of the Benevento province were classified as areas sensitive to nutrient and contaminant leaching.

show abstract

A BME solution of the inverse problem for saturated groundwater flow

Cited by 30 publications

References 12 publications

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

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

Space–time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona

Environinformatics in ecological risk assessment of agroecosystems pollutant leaching

Contact Info

Product

Resources

About