ABSTRACT A large number of predictor variables can be used in digital soil mapping; however, the presence of irrelevant covariables may compromise the prediction of soil types. Thus, algorithms can be
We consider the issue of forecast failure (or breakdown) and propose methods to assess retrospectively whether a given forecasting model provides forecasts which show evidence of changes with respect
aMI, mutual information. The mutual information between variables A and B (in this study, A and B represent two methods' prediction results) is calculated as MI(A,B) = H(A)+H(B)?H(A,B) where and [p(a)
