Abstract
The paper presents examples of using ANNs and other machine learning (ML) techniques to assess uncertainty of a mathematical (computer-based) model M. Two approaches have been developed to estimate parametric and residual uncertainty, and they were tested on process based hydrological models. One approach emulates computationally expensive Monte Carlo simulations, and the second one uses residuals of a calibrated model M outputs to assess the remaining uncertainty of this model. ML models are trained to approximate the functional relationships between the input (and state) variables of the model M and the uncertainty descriptors. ML model, being trained, encapsulates the information about the model M errors specific for different conditions in the past, and is used to estimate the probability distribution of the model M error for the new model runs. Methods are tested to estimate uncertainty of a conceptual rainfall-runoff model of a catchment in UK.
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Shrestha, D.L., Kayastha, N., Solomatine, D.P. (2009). ANNs and Other Machine Learning Techniques in Modelling Models’ Uncertainty. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_39
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DOI: https://doi.org/10.1007/978-3-642-04277-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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