ANN-based representation of parametric and residual uncertainty of models

In this paper we investigate the possibility of using ANN for modeling uncertainty of models (with some focus on environmental models). We assume that all uncertainties of the prediction made by such model M are represented by probability distribution function (pdf) of its error, and build regression models of the quantiles of this pdf. The original version of the technique termed UNEEC (published earlier) deals with residual uncertainty of calibrated (trained) deterministic models, and uses fuzzy clustering and soft weighting of local models to deal with the fact that uncertainty of environmental models is different in different regions of the state space. The extended version of the method presented here allows also for explicit handling the uncertainty in parameters of environmental model M. The resulting ANN encapsulates both the results of Monte-Carlo simulations as well as the residual uncertainty. On two data sets it is shown that the presented approach allows for generating consistent predictions of models uncertainty.