Reference:Reference-025516: Difference between revisions
From CSDMS
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{{ | {{BibEntryCargo | ||
|Firstname=Feng Chang, Christina; Astitha, Marina; Yuan, Yongping; Tang, Chunling; Vlahos, Penny; Garcia, Valerie; Khaira, Ummul; | |||
|BibType=journalArticle | |BibType=journalArticle | ||
|Title=A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems | |Title=A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems | ||
|Year=2023-07 | |Year=2023-07 | ||
|Journal=Artificial Intelligence for the Earth Systems | |Journal=Artificial Intelligence for the Earth Systems | ||
|Booktitle= | |Booktitle= | ||
|Volume=2 | |Volume=2 | ||
|Pages= | |Pages= | ||
|URL=https://journals.ametsoc.org/view/journals/aies/2/3/AIES-D-22-0049.1.xml | |URL=https://journals.ametsoc.org/view/journals/aies/2/3/AIES-D-22-0049.1.xml | ||
|DOI=10.1175/AIES-D-22-0049.1 | |DOI=10.1175/AIES-D-22-0049.1 | ||
|Note=Auto downloaded ref at: 2024-09-04 | |Note=Auto downloaded ref at: 2024-09-04 | ||
}} | }} | ||
{{ | {{RefsInt1 | ||
|FeatureRef=No | |||
|PublicationClusterID=0 | |PublicationClusterID=0 | ||
|MS_PublicationClusterID=0 | |MS_PublicationClusterID=0 | ||
|Semantic_ID=426dc20663fdf84fbf071c4bf4d83b15dd8a3182 | |Semantic_ID=426dc20663fdf84fbf071c4bf4d83b15dd8a3182 |CorpusID=258880317 | ||
|CorpusID=258880317 | |||
|PublicationWhatKindOf=a module application description | |PublicationWhatKindOf=a module application description | ||
|PublicationNrofModels=a single module | |PublicationNrofModels=a single module | ||
|PublicationHPCCYesno=No | |PublicationHPCCYesno=No | ||
}} | }} | ||
{{ | {{RefsInt2 | ||
|PublicationMultipleModelsCargo=WRF | |||
}} | }} | ||
{{ | {{RefsInt3 | ||
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Latest revision as of 14:52, 12 November 2024
| Author(s) | Feng Chang, Christina; Astitha, Marina; Yuan, Yongping; Tang, Chunling; Vlahos, Penny; Garcia, Valerie; Khaira, Ummul; |
| BibType | journalArticle |
| Title | A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems |
| Editors | |
| Year | 2023-07 |
| Journal | Artificial Intelligence for the Earth Systems |
| Booktitle | |
| Volume | 2 |
| Pages | |
| URL | https://journals.ametsoc.org/view/journals/aies/2/3/AIES-D-22-0049.1.xml |
| DOI | 10.1175/AIES-D-22-0049.1 |
| ISBN | |
| Note | Auto downloaded ref at: 2024-09-04
|
| |
|
| Feature reference | No |
| PublicationClusterID | 0 |
| MS_PublicationClusterID | 0 |
| Semantic_ID | 426dc20663fdf84fbf071c4bf4d83b15dd8a3182 |
| Nr of citations | 2 |
| 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: | WRF
|
