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Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models, Nagendra Kayastha, Dimitri P. Solomatine, Durga Lal Shrestha

International Conference on Hydroinformatics

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the …