Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: a case study in Taihu Lake, China
Chlorophyll-a (Chl-a) is an important index in water quality assessment by remote sensing technology. For the study of Chl-a value measurement in rivers or lakes, there are many classical methods, such as curve fitting, back propagation (BP) neural network and radial basis function (RBF) neural netw...
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
IWA Publishing
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/a06c06be6e1f41fbbfd7b6ed7f2b7923 |
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