Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)-Reference-Cited by-同舟云学术

Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)

Published:2022-10-12 Issue: Volume: Page:72-85
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
Short-container-title:

Author:

González-Enrique Javier^ORCID,Ruiz-Aguilar Juan Jesús^ORCID,Madrid Navarro Eduardo,Martínez Álvarez-Castellanos Rosa,Felis Enguix Ivan,Jerez José M.,Turias Ignacio J.^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-18050-7_8

Reference21 articles.

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2. Ruiz, J.M., et al.: Informe de evolución y estado actual del Mar Menor en relación al proceso de eutrofización y sus causas. Informe de asesoramiento técnico del Instituto Español de Oceanografía (IEO) (2022)

3. U. P. Cartagena Servidor de Datos Cientificos del Mar Menor. https://marmenor.upct.es/docs/. Accessed 6 May 2022

4. Jimeno-Sáez, P., Senent-Aparicio, J., Cecilia, J., Pérez-Sánchez, J.: Using machine-learning algorithms for eutrophication modeling: case study of Mar Menor Lagoon (Spain). Int. J. Environ. Res. Public Health 17, 1189 (2020)

5. Yu, Z., Yang, K., Luo, Y., Shang, C.: Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network. J. Hydrol. 582, 124488 (2020)

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