A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling SystemsSource: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 003Author:Feng Chang, Christina
,
Astitha, Marina
,
Yuan, Yongping
,
Tang, Chunling
,
Vlahos, Penny
,
Garcia, Valerie
,
Khaira, Ummul
DOI: 10.1175/AIES-D-22-0049.1Publisher: American Meteorological Society
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| contributor author | Feng Chang, Christina | |
| contributor author | Astitha, Marina | |
| contributor author | Yuan, Yongping | |
| contributor author | Tang, Chunling | |
| contributor author | Vlahos, Penny | |
| contributor author | Garcia, Valerie | |
| contributor author | Khaira, Ummul | |
| date accessioned | 2024-12-24T14:12:09Z | |
| date available | 2024-12-24T14:12:09Z | |
| date copyright | 01 Jul. 2023 | |
| date issued | 2023 | |
| identifier other | aies-AIES-D-22-0049.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4300333 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems | |
| type | Journal Paper | |
| journal volume | 2 | |
| journal issue | 3 | |
| journal title | Artificial Intelligence for the Earth Systems | |
| identifier doi | 10.1175/AIES-D-22-0049.1 | |
| journal lastpage | e220049 | |
| tree | Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 003 | |
| contenttype | Fulltext |