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A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems

dc.contributor.authorFeng Chang, Christina
dc.contributor.authorAstitha, Marina
dc.contributor.authorYuan, Yongping
dc.contributor.authorTang, Chunling
dc.contributor.authorVlahos, Penny
dc.contributor.authorGarcia, Valerie
dc.contributor.authorKhaira, Ummul
dc.date.accessioned2024-12-24T14:12:09Z
dc.date.available2024-12-24T14:12:09Z
dc.date.copyright01 Jul. 2023
dc.date.issued2023
dc.identifier.otheraies-AIES-D-22-0049.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4300333
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleA New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems
dc.typeJournal Paper
dc.journal.volume2
dc.journal.issue3
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-22-0049.1
dc.journal.lastpagee220049
dc.treeArtificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 003
dc.contenttypeFulltext


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