Exploring a multi-output temporal convolutional network driven encoder-decoder framework for ammonia nitrogen forecasting.

Sheng, Sheng; Lin, Kangling; Zhou, Yanlai; Chen, Hua; Luo, Yuxuan; Guo, Shenglian; Xu, Chong-Yu

Sheng, Sheng; Lin, Kangling; Zhou, Yanlai; Chen, Hua; Luo, Yuxuan; Guo, Shenglian; Xu, Chong-Yu.

Afiliación

Sheng S; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
Lin K; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
Zhou Y; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China. Electronic address: yanlai.zhou@whu.edu.cn.
Chen H; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China. Electronic address: chua@whu.edu.cn.
Luo Y; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
Guo S; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
Xu CY; Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, N-0316, Oslo, Norway.

J Environ Manage ; 342: 118232, 2023 Sep 15.

Article en En | MEDLINE | ID: mdl-37270980

Asunto(s)

Amoníaco; Monitoreo del Ambiente; Monitoreo del Ambiente/métodos; China; Reproducibilidad de los Resultados; Modelos Teóricos; Nitrógeno/análisis; Predicción

Palabras clave

Artificial neural networks (ANNs); Deep learning; Encoder-decoder structure; Sequence-to-Sequence; Water quality forecast

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Amoníaco Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: J Environ Manage Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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