Reference:Reference-029480: Difference between revisions

From CSDMS
m Text replacement - "|Semantic_ID=ccf0e233c81f4eaac6c388b1db86d39d17a21bcc" to "|Semantic_ID=ccf0e233c81f4eaac6c388b1db86d39d17a21bcc |CorpusID=268712388"
m Text replacement - " |CorpusID=" to " |CorpusID="
Line 16: Line 16:
|PublicationClusterID=0
|PublicationClusterID=0
|MS_PublicationClusterID=0
|MS_PublicationClusterID=0
|Semantic_ID=ccf0e233c81f4eaac6c388b1db86d39d17a21bcc |CorpusID=268712388
|Semantic_ID=ccf0e233c81f4eaac6c388b1db86d39d17a21bcc
|CorpusID=268712388
|PublicationWhatKindOf=a module application description
|PublicationWhatKindOf=a module application description
|PublicationNrofModels=a single module
|PublicationNrofModels=a single module

Revision as of 10:38, 1 October 2024



Author(s) Yifru, Bisrat Ayalew; Lim, Kyoung Jae; Bae, Joo Hyun; Park, Woonji; Lee, Seoro;
BibType journalArticle
Title A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling
Editors
Year 2024-04-01
Journal Hydrology Research
Booktitle None
Volume 55
Pages 498–518
URL https://iwaponline.com/hr/article/55/4/498/101195/A-hybrid-deep-learning-approach-for-streamflow
DOI 10.2166/nh.2024.016
ISBN
Note Auto downloaded ref at: 2024-09-07


 
Feature reference No
PublicationClusterID 0
MS_PublicationClusterID 0
Semantic_ID ccf0e233c81f4eaac6c388b1db86d39d17a21bcc
Nr of citations 0
Sort of publication a module application description
Sort of model publication a single module
Is the CSDMS HPC used No
If HPC is used, for what project was it?:
Associated simulation movie if any:


Model(s) discussed: SWAT