Hi community,
I'm currently trying to find the best and most accurate Kriging-interpolation for my project: interpolation of a groundwater-surface in an unconfined aquifer.
My project-area is a city-district of Vienna, Austria. My aquifer is mostly homogenous, consisting of gravel and sand and shows a slight trend of decreasing groundwater-levels in ESE direction.
So far, so good.
I've now tried "ordinary kriging" with gaussian kernel function and applied different settings (number of sectors, size of lags, etc.).
My problem now (please correct me if I'm wrong):
For assessing the quality of my results, I focus on the following factors:
1. Root-mean-square (should be as small as possible)
2. Root-mean-square-standardized (should be as close to 1 as possible)
3. Mean standardized error (should be as small as possible)
4. Average standard error (should be as small as possible)
Right?
But which of these factors is most important?
I'm currently facing the problem, that I have cases, in which RMS and MSE are close to 0; RMSS) is close to 1, BUT average standard error is relatively high..... and cases where it is the other way round.
And another question:
Is Ordinary Kriging a good usable method for modeling groundwater-levels anyway?
I also thought about using external drift-Kriging perhaps, but as I'd probably have to use the surface-model as 2nd parameter, it doesn't seem to make sense (urban flat-lands, low natural differences in elevation, where human-caused changes in elevation are probably higher than natural changes).