A comparison of Multiple Non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic ocean based on satellite data
Using measurements of Sea Surface Salinity and Sea Surface Temperature in the Western Tropical Atlantic Ocean, from 2003 to 2007 and 2009, we compare two approaches for estimating Sea Surface Salinity : Multiple Non-linear Regression and Multi Layer Perceptron. In the f...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2015-02-01
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| Series: | ESAIM: Proceedings and Surveys |
| Online Access: | http://dx.doi.org/10.1051/proc/201549006 |
| Summary: | Using measurements of Sea Surface Salinity and Sea Surface Temperature in the Western
Tropical Atlantic Ocean, from 2003 to 2007 and 2009, we compare two approaches for
estimating Sea Surface Salinity : Multiple Non-linear Regression and Multi Layer
Perceptron. In the first experiment, we use 18,300 in situ data points to
establish the two models, and 503 points for testing their extrapolation.
In the second experiment, we use 15,668 in situ measurements for
establishing the models, and 3,232 data points to test their
interpolation. The results show that the Multiple Non-linear Regression
is an admissible solution whether it be interpolation or
extrapolation. Yet, the Multi Layer Perceptron can be used only for
interpolation. |
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| ISSN: | 2267-3059 |