Reference:Reference-025516

{{|Firstname=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 |Year=2023-07 |Journal=Artificial Intelligence for the Earth Systems |Booktitle=None |Volume=2 |Pages=None |URL=https://journals.ametsoc.org/view/journals/aies/2/3/AIES-D-22-0049.1.xml |DOI=10.1175/AIES-D-22-0049.1 |Note=Auto downloaded ref at: 2024-09-04 }} {{|FeatureRef=No |PublicationClusterID=0 |MS_PublicationClusterID=0 |Semantic_ID=426dc20663fdf84fbf071c4bf4d83b15dd8a3182 |CorpusID=258880317 |PublicationWhatKindOf=a module application description |PublicationNrofModels=a single module |PublicationHPCCYesno=No }} {{|PublicationMultipleModelsCargo=WRF }} {{|PublicationAnimationsCargo=None }}