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

Chang, Christina Feng; Astitha, Marina; Yuan, Yongping; Tang, Chunling; Vlahos, Penny; Garcia, Valerie; Khaira, Ummul.

Afiliação

Chang CF; Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut.
Astitha M; Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut.
Yuan Y; Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.
Tang C; Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.
Vlahos P; Department of Marine Sciences, University of Connecticut, Groton, Connecticut.
Garcia V; Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut.
Khaira U; Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut.

Artif Intell Earth Syst ; 2(3): 1-20, 2023 Jul 01.

Article em En | MEDLINE | ID: mdl-37841557