Use of Evidence Likelihood transformation


I am going through the tutorial EXERCISE 4-2 of LCM and have a question on the use of data transformed by Evidence Likelihood transformation as a driver variable in LCM (esp. page 324). I understood that Evidence Likelihood transformation derives relative frequency with which different land cover categories occurred within the areas that transitioned from 1986 to 1994. The, we use it as one of driver variables to train MLP Neural Network submodel.

My colleague mentioned that it does not seem to be appropriate to include variable transformed with Evidence Likelihood transformation in the training of MLP Neural Network Sub-model because the transformed variable is similar to what the Sub-model is trying to predict, and looks tautological process. 

I found a couple of journal articles that utilized LCM with a transformed driver variable through Evidence Likelihood transformation, which suggested the process is methodologically acceptable. However, the point my colleague raised sounds also reasonably.

I am still looking for the scientific explanation that justifies the use of a driver variable, transformed with Evidence Likelihood transformation, in the training of MLP Neural Network Sub-model.






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