Dear Recep, I don't think the type of variable has any impact on the choice of the detrending algorithm. There is indeed a whole collection of methods and one can also cite the various methods in which the trend is modelled by neural networks (e.g. Kanevski's NNRK Neural Network Residual Kriging). NN can be very useful for complex detrending but less, in my eyes at least, if you want to understand the mathematical/physical expression of your drift. If you do care, then I would propose to use more simple polynomial functions that may be more easy to understand when analysing the drift.
You may find the following report useful Hengl, T., Heuvelink, G.B.M. and Stein, A., 2003. Comparison of kriging with external drift and regression-kriging. Technical report, International Institute for Geo-information Science and Earth Observation (ITC), Enschede, pp. 18. http://www.itc.nl/library/Papers_2003/misca/hengl_comparison.pdf The first author has also on the web a practical guide to regression-kriging explaining how to do it with various software. See http://hengl.pfos.hr/RKguide.php Hope this helps, Gregoire -----Original Message----- From: Recep kantarci [mailto:[EMAIL PROTECTED] Sent: 30 June 2005 15:39 To: [email protected] Subject: [ai-geostats] modelling trend and kriging type Dear ai-geostats members When the data used has a trend, it is needed to model trend and in this case there exists various types of kriging to apply (universal kriging, kriging with a trend, regression kriging etc). If this is the case, does one should use the same type of kriging or different depending on modeling the trend using coordinates of target variable or using other (namely, secondary or auxillary) variables such as elevation or topography ? That is , are there a dinstinction depending on the type of variables to model the trend while kriging? Best regards Recep Yahoo! kullaniyor musunuz? Istenmeyen postadan biktiniz mi? Istenmeyen postadan en iyi korunma Yahoo! Posta'da http://tr.mail.yahoo.com
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