The models’ training parameters.

<div><p>Flood forecasting exhibits rapid fluctuations, water level forecasting shows great uncertainty and inaccuracy in small watersheds, and the reliability and accuracy performance of traditional probability forecasting is often unbalanced. This study combined Recurrent Neural Network...

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Bibliographic Details
Main Author:	Songsong Wang (8088293) (author)
Other Authors:	Ouguan XU (21156224) (author)
Published:	2025
Subjects:	Medicine Neuroscience Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified gated recurrent unit extreme water level water level forecasting traditional probability forecasting forecasting hourly streamflow achieved reasonable balance compound rnn could bayesian </ p reasonable reliability zhejiang province weight training time windows term memory small watersheds small watershed qixi reservoir often unbalanced long short including hydrology good alternative comparative analysis best method bayesian structure base rnn 5 days 31 %, 15 %,
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The models’ training parameters.

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