Phases of RNN models’ train, validation and prediction.
<p>Phases of RNN models’ train, validation and prediction.</p>
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2025
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| _version_ | 1852021181115269120 |
|---|---|
| author | Songsong Wang (8088293) |
| author2 | Ouguan XU (21156224) |
| author2_role | author |
| author_facet | Songsong Wang (8088293) Ouguan XU (21156224) |
| author_role | author |
| dc.creator.none.fl_str_mv | Songsong Wang (8088293) Ouguan XU (21156224) |
| dc.date.none.fl_str_mv | 2025-04-21T17:44:06Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321583.g009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Phases_of_RNN_models_train_validation_and_prediction_/28833383 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 %, |
| dc.title.none.fl_str_mv | Phases of RNN models’ train, validation and prediction. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Phases of RNN models’ train, validation and prediction.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_20faeb8fb32b8831eb5b8a2c1e95bdac |
| identifier_str_mv | 10.1371/journal.pone.0321583.g009 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28833383 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Phases of RNN models’ train, validation and prediction.Songsong Wang (8088293)Ouguan XU (21156224)MedicineNeuroscienceBiotechnologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedgated recurrent unitextreme water levelwater level forecastingtraditional probability forecastingforecasting hourly streamflowachieved reasonable balancecompound rnn couldbayesian </ preasonable reliabilityzhejiang provinceweight trainingtime windowsterm memorysmall watershedssmall watershedqixi reservoiroften unbalancedlong shortincluding hydrologygood alternativecomparative analysisbest methodbayesian structurebase rnn5 days31 %,15 %,<p>Phases of RNN models’ train, validation and prediction.</p>2025-04-21T17:44:06ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321583.g009https://figshare.com/articles/figure/Phases_of_RNN_models_train_validation_and_prediction_/28833383CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288333832025-04-21T17:44:06Z |
| spellingShingle | Phases of RNN models’ train, validation and prediction. Songsong Wang (8088293) 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 %, |
| status_str | publishedVersion |
| title | Phases of RNN models’ train, validation and prediction. |
| title_full | Phases of RNN models’ train, validation and prediction. |
| title_fullStr | Phases of RNN models’ train, validation and prediction. |
| title_full_unstemmed | Phases of RNN models’ train, validation and prediction. |
| title_short | Phases of RNN models’ train, validation and prediction. |
| title_sort | Phases of RNN models’ train, validation and prediction. |
| topic | 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 %, |