Reference:Reference-014667: Difference between revisions
From CSDMS
m Text replacement - "|Semantic_ID=0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe" to "|Semantic_ID=0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe |CorpusID=236313868" |
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Latest revision as of 11:29, 8 October 2024
| Author(s) | Kim, Taereem; Yang, Tiantian; Gao, Shang; Zhang, Lujun; Ding, Ziyu; Wen, Xin; Gourley, Jonathan J.; Hong, Yang; |
| BibType | journalArticle |
| Title | Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS |
| Editors | |
| Year | 2021-07 |
| Journal | Journal of Hydrology |
| Booktitle | |
| Volume | 598 |
| Pages | 126423 |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0022169421004704 |
| DOI | 10.1016/j.jhydrol.2021.126423 |
| ISBN | |
| Note | Auto downloaded ref at: 2021-07-03
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| Feature reference | No |
| PublicationClusterID | 0 |
| MS_PublicationClusterID | 3161929512 |
| Semantic_ID | 0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe |
| Nr of citations | 70 |
| 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: |
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| Model(s) discussed: | CREST |
