Area-class maps are conventionally delineated by human hands based on limited observed (or high-quality) data and expert knowledge. It is recognized that areaclass maps contain spatial uncertainty because the classification of unobserved locations is not completely certain. Spatial uncertainty associated with an area-class map may be quantified by spatial statistical approaches through estimati...

Traditional spectral classification of remote sensing data applied on per pixel basis ignores the potentially useful spatial information between the values of proximate pixels. Although spatial information extraction has been greatly explored, there have been limited attempts to enhance classification by combining spectral and spatial information. This improvement would arise from the hypothesi...

We describe a prototype approach to flexible modelling for maxima observed at sites in a spatial domain, based on fitting of max-stable processes derived from underlying Gaussian random fields. The models we propose have generalized extreme-value marginal distributions throughout the spatial domain, consistent with statistical theory for maxima in simpler cases, and can incorporate both geostat...

Biography Dr. Dan Cornford is a lecture in Computer Science and works in the Neural Computing Research Group at Aston University. Research interests are in the field of spatial statistics, space-time modelling and data assimilation. Lehel Csato is a post-doc in the same group working on an EPSRC grant (GR/R61857/01) looking at applying sparse sequential Gaussian processes to data assimilation. ...

Geostatistics has become a preferred tool for the identification of lithofacies from sparse data, such as measurements of hydraulic conductivity and porosity. Recently we demonstrated that the support vector machine (SVM), a tool from machine learning, can be readily adapted for this task, and offers significant advantages. On the conceptual side, the SVM avoids the use of untestable assumption...

Temporal and Spatial Modeling of Groundwater Level in Bushehr Plain using Artificial Intelligence Geostatistics

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly...

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understan...