Marine chlorophyll-a prediction based on deep auto-encoded temporal convolutional network model-Reference-Cited by-同舟云学术

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Marine chlorophyll-a prediction based on deep auto-encoded temporal convolutional network model

Published:2023-12 Issue: Volume:186 Page:102263
ISSN:1463-5003
Container-title:Ocean Modelling
language:en
Short-container-title:Ocean Modelling

Author:

Ying Chen,Xiao Li,Xueliang Zhao,Wenyang Song,Chongxuan Xu

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Atmospheric Science,Geotechnical Engineering and Engineering Geology,Computer Science (miscellaneous),Oceanography

Reference28 articles.

1. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model;Barzegar;Stoch. Environ. Res. Risk Assess.,2020

2. Consensus methods based on machine learning techniques for marine phytoplankton presence–absence prediction;Bourel;Ecol. Inform.,2017

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1. Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method;Marine Pollution Bulletin;2024-09

2. Optimization of convolutional neural network with dual attention mechanism: Estimation of chlorophyll-a concentration in the Taiwan Strait using MODIS data;Estuarine, Coastal and Shelf Science;2024-05

3. DETERMINAÇÃO DO TEOR DE CLOROFILA DOS EXTRATOS DAS FOLHAS DE PIPER MARGINATUM JACQ COLETADAS EM ITACOATIARA POR MEIO DA ESPECTROFOTOMETRIA UV-VIS.;Revista Multidisciplinar do Nordeste Mineiro;2024-04-30

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