Hydrology, Volume 9, Issue 1
2022 January - 16 articles
Cover Story: This study investigates the use of a machine learning (ML) model as an independent tool to assess the performance of hydrological models. The ML model is based on recurrent neural networks and has a simple topology, so it can be easily implemented (in some ML toolboxes without writing a single line of code). The ML model processes the outputs and inputs of a hydrological model and concludes on whether the hydrological model error is uncorrelated, homoscedastic, and zero-inflated. If so, the assessed hydrological model successfully captures all information in the available data. Thus, the hydrologist can be confident that the hydrological model has achieved the best feasible performance. If this is not the case, they can redesign the hydrological model in order to improve its capacity and repeat the assessment procedure. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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