EGU21-16249, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

An ensemble of deep learning models with data assimilation for hydrologic forecasting

Seong Jin Noh, Hyeonjin Choi, and Bomi Kim
Seong Jin Noh et al.
  • Kumoh National Institute of Technology, Department of Civil Engineering, Gumi-si, Korea, Republic of (

We present an approach to combine two data-centric approaches, data assimilation (DA) and deep learning (DL), from the perspective of hydrologic forecasting. DA is a statistical approach based on Bayesian filtering to produce optimal states and/or parameters of a dynamic model using observations. By extracting information from both model and observational data, DA improves not only the performance of numerical modeling, but also understanding of uncertainties in predictions. While DA complements information gaps in model and observational data, DL constructs a new modeling system by extracting and abstracting information solely from data without relying on the conventional knowledge of hydrologic systems. In a new approach, an ensemble of deep learning models can be updated by real-time data assimilation when a new observation becomes available. In the presentation, we will focus on discussing the potentials of combining two data-centric approaches.


How to cite: Noh, S. J., Choi, H., and Kim, B.: An ensemble of deep learning models with data assimilation for hydrologic forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16249,, 2021.


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