Dear AI-GEOSTATiSticians,
My research is on heavy metal pollution in water bodies.
As a part of the analysis, I am doing kriging with the pollutant data.
I have couple of problems in doing this task.
1. Though I have the data sets for 90 water bodies, most of them (85) have
data points
less than 10. As one can expect, this 'environment' gives trouble in
fitting variograms.
Two papers, I came across on similar issue haven't help me much in solving
the problem.
Is there any consistent tested way to approach such 'not-enough-data'
situations?
2. Some data are with 'Hot Spots'. However, when I work with the data sets,
I have the trouble in fitting the variogram. My questions may be trivial ones.
How to distinguish Outliers from Hotspots, if there is a lack of
site-information beyond the data set?
Could it be possible to effectively fit variograms, when the hot spots are
present?
Could a variogram capture the hot spot presence for kriging?
[ For most of the cases I tried with such suspected hotspot data, my
results show that
the linear interpolation works better than the krigged distribution based
on the 'fitted' variograms]
I would appreciate if anyone could provide me some suggestions on the above
difficulties, and relevant
references for my reading
Thank you very much.
Regards,
-/Ramanitharan, K.
=====
Ramanitharan Kandiah
Graduate Student
Department of Civil & Environmental Engineering
Tulane University
New Orleans, LA 70125
USA.
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