Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction

Investigated machine-learning algorithms for streamflow prediction, surpassing traditional hydrological models. Proposed a methodology for testing and benchmarking ML algorithms using artificial data. Con-cluded that deep learning can identify the transformation function but may not significantly outperform temporal persistence in Forecast Mode.

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