Validation#

Cross-validation#

validate_kriging(points, theoretical_model, how='ok', neighbors_range=None, no_neighbors=4, use_all_neighbors_in_range=False, sk_mean=None, allow_approx_solutions=False)[source]

Function performs cross-validation of kriging models.

Parameters:

pointsnumpy array

Known points and their values.

theoretical_modelTheoreticalVariogram

Fitted variogram model.

howstr, default=’ok’

Select what kind of kriging you want to perform:

‘ok’: ordinary kriging,
‘sk’: simple kriging - if it is set then sk_mean parameter must be provided.

neighbors_rangefloat, default=None

The maximum distance where we search for neighbors. If None is given then range is selected from the theoretical_model rang attribute.

no_neighborsint, default = 4

The number of the n-closest neighbors used for interpolation.

use_all_neighbors_in_rangebool, default = False

True: if the real number of neighbors within the neighbors_range is greater than the number_of_neighbors parameter then take all of them anyway.

sk_meanfloat, default=None

The mean value of a process over a study area. Should be know before processing. That’s why Simple Kriging has a limited number of applications. You must have multiple samples and well-known area to know this parameter.

allow_approx_solutionsbool, default=False

Allows the approximation of kriging weights based on the OLS algorithm. We don’t recommend set it to True if you don’t know what are you doing. This parameter can be useful when you have clusters in your dataset, that can lead to singular or near-singular matrix creation.

Returns:

: Tuple

Function returns tuple with:

Mean Prediction Error,
Mean Kriging Error: ratio of variance of prediction errors to the average variance error of kriging,
array with: [coordinate x, coordinate y, prediction error, kriging estimate error]

References

Clark, I., (2004) “The Art of Cross Validation in Geostatistical Applications”
Clark I., (1979) “Does Geostatistics Work”, Proc. 16th APCOM, pp.213.-225.