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Comparing machine learning methods in estimation of model uncertainty

The paper presents a generalization of the framework for assessment of predictive models uncertainty using machine learning techniques. Historical model errors which are mismatch between observed and predicted values are assumed to be indicators of total model uncertainty; it is measured in the form of prediction intervals, and comprises all sources of uncertainty including model structure, model parameters, input and output data. Several machine learning methods are compared. The approach is tested on a conceptual hydrological model set up to predict stream flows of the Brue catchment in the United Kingdom.