Improving the reliability of probabilistic multi-step-ahead flood forecasting by fusing unscented kalman filter with recurrent neural_network

HIGHLIGHTS

  • who: Gorges Reservoir and colleagues from the State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan have published the article: Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural_Network, in the Journal: (JOURNAL)
  • what: This study proposes a probabilistic forecasting approach to reduce the prediction intervals of multi-step-ahead flood forecasts, which consists of two parts: the deterministic forecast model and the probabilistic post-processing technique.
  • how: In this study . . .

     

    Logo ScioWire Beta black

    If you want to have access to all the content you need to log in!

    Thanks :)

    If you don't have an account, you can create one here.

     

Scroll to Top

Add A Knowledge Base Question !

+ = Verify Human or Spambot ?