Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model

The improved accuracy provides a chance to identify the most relevant variables of the problem. The procedure was to add/remove one after the other the variables and perform from scratch the corresponding training of the ANN. After some trial and error as well as phenomenological arguments, we were able to show that some popular meteorological variables like mean relative humidity and mean temperature shown no relevance while the results were clearly improved by including the hours with RH < 40%. The results as such might be valid for a limited geographical region, but the procedure is completely general and applicable to other regions.