Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions.

Xiong, Rui; Zheng, Yi; Chen, Nengwang; Tian, Qing; Liu, Wei; Han, Feng; Jiang, Shijie; Lu, Mengqian; Zheng, Yan

Xiong, Rui; Zheng, Yi; Chen, Nengwang; Tian, Qing; Liu, Wei; Han, Feng; Jiang, Shijie; Lu, Mengqian; Zheng, Yan.

Afiliação

Xiong R; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Zheng Y; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China.
Chen N; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Tian Q; Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen 518055, Guangdong Province, China.
Liu W; Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
Han F; Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
Jiang S; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Lu M; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Zheng Y; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Environ Sci Technol ; 56(14): 10530-10542, 2022 07 19.

Article em En | MEDLINE | ID: mdl-35772808

Assuntos

Nitrogênio; Rios; Inteligência Artificial; China; Monitoramento Ambiental; Fertilizantes; Humanos; Nitrogênio/análise

Palavras-chave

LSTM; artificial intelligence; deep learning; nitrogen; nonpoint sources; riverine export; transfer learning

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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Rios / Nitrogênio Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Environ Sci Technol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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