Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage.

Pyo, JongCheol; Cho, Kyung Hwa; Kim, Kyunghyun; Baek, Sang-Soo; Nam, Gibeom; Park, Sanghyun

Pyo, JongCheol; Cho, Kyung Hwa; Kim, Kyunghyun; Baek, Sang-Soo; Nam, Gibeom; Park, Sanghyun.

Afiliação

Pyo J; Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Republic of Korea.
Cho KH; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Republic of Korea.
Kim K; Watershed and Total Load Management Research Division, National Institute of Environmental Research, Incheon 22689, Republic of Korea.
Baek SS; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Republic of Korea.
Nam G; Water Quality Assessment Research Division, National Institute of Environmental Research, Incheon 22689, Republic of Korea. Electronic address: gbnam@korea.kr.
Park S; Water Quality Assessment Research Division, National Institute of Environmental Research, Incheon 22689, Republic of Korea. Electronic address: pbaby75@korea.kr.

Water Res ; 203: 117483, 2021 Sep 15.

Article em En | MEDLINE | ID: mdl-34384949

Assuntos

Cianobactérias; Aprendizado Profundo; Ecossistema; Monitoramento Ambiental; Qualidade da Água

Palavras-chave

Cyanobacteria cell; Hydrodynamic model; Hyperspectral image; Interpretable deep learning model; Prediction

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Cianobactérias / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google