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Application of deep learning in predicting suspended sediment concentration: A case study in Jiaozhou Bay, China.
Xie, Jianbin; Feng, Xingru; Gao, Tianhai; Wang, Zhifeng; Wan, Kai; Yin, Baoshu.
Afiliación
  • Xie J; Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; University of Chinese Academy of Sciences, Beijing 100029, China.
  • Feng X; Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Pilot National Laboratory for Marine Science and Technology, Qingdao 266071, China; University of Chinese Academy of Scienc
  • Gao T; Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; University of Chinese Academy of Sciences, Beijing 100029, China.
  • Wang Z; Ocean University of China, Qingdao 266071, China.
  • Wan K; North China Sea Survey Center, MNR, Qingdao 266071, China.
  • Yin B; Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Pilot National Laboratory for Marine Science and Technology, Qingdao 266071, China; University of Chinese Academy of Scienc
Mar Pollut Bull ; 201: 116255, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38513605
ABSTRACT
Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5-0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Aprendizaje Profundo País/Región como asunto: Asia Idioma: En Revista: Mar Pollut Bull Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Aprendizaje Profundo País/Región como asunto: Asia Idioma: En Revista: Mar Pollut Bull Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido