I have to establish an ML-based model where I predict precipitation in a complex terrain using multi-year daily observations from 50 stations. Besides a dozen of continuous variables, predictors include three variables that reflect topography: elevation, slope, and aspect. As these three variables do not change for a single station, I have doubts that the model will count on these during the training (I haven't yet started the analysis, still compiling the data frame).
- Are my concerns valid?
I'm thinking about writing a function that will randomly alter these three static variables per each observation in a data frame by a small margin, e.g +-2%.
- Would there be major caveats behind such an approach?