Research on Geophysical Modeling Using Extreme Learning Machine for Scatterometer

Bo Heng Duan; Wei Min Zhang; Cheng Zhang Zhu

doi:10.4028/www.scientific.net/AMM.644-650.1625

Paper Titles

New Variational Iteration Procedure for some Parabolic Equations with Fourth-Order Derivatives
p.1585

Research on Relative Reducts of Rough Set Model in Consistent and Inconsistent Decision Tables
p.1590

A Novel Approach Based on Rough Conditional Entropy for Attribute Reduction
p.1607

A Statistics-Based Data Placement Strategy for Hybrid Storage
p.1620

Research on Geophysical Modeling Using Extreme Learning Machine for Scatterometer
p.1625

Research on Knowledge Resource Clustering Based on K-MEAN Algorithm
p.1629

Study on Model for Structural Reliability Based on Dynamic Interfering Theory
p.1633

A Novel Twin Support Vector Machines with Manifold Regularization
p.1640

The Special Form of Second Order Accuracy N-S Equations for Incompressible Fluid
p.1644

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Research on Geophysical Modeling Using Extreme...

Research on Geophysical Modeling Using Extreme Learning Machine for Scatterometer

Abstract:

Using the quantitative geophysical model function (GMF) between the radar backscatter coefficient and the sea surface wind speed, wind direction, radar parameters and environmental parameters, the wind vector can be retrieved from backscattering measurement. In this paper, Extreme learning machine (ELM) approach is used to develop a unified GMF respectively using the simulated training data-set generated by the empirical GMF CMOD5.N and the wind data gained from the ASCAT. Analysis indicates that the method based on extreme learning machine showing a good inversion result compared with CMOD5.N with fast training and high accuracy. The new method provides a novel feasible way for future surface wind field inversion.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

1625-1628

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.1625

Citation:

Cite this paper

Online since:

September 2014

Authors:

Bo Heng Duan*, Wei Min Zhang, Cheng Zhang Zhu

Keywords:

CMOD5.N, Elm, Geophysical Model Function, Scatterometer

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] NADERI, FM, FREILICH, MH. Space borne radar measurement of wind velocity over the ocean: An overview of the NSCAT scatterometer system [J]. Proceedings of the IEEE, 1991, 79 (6): 850 - 866.

DOI: 10.1109/5.90163

Google Scholar

[2] Zou Juhong, LIN Mingsen, Pan Delu. A unified geophysical model of C -band and Ku-band based on neural networks [J]. Oceanography, 2008, 30 (5): 23-28.

Google Scholar

[3] Xie Xuetong, Fang Yu, Chen Kehai. Sea surface wind field modeling of SeaWinds based on neural network [J]. Geography and Geo-Information Science, 2007, 23 (2): 12 - 17.

Google Scholar

[4] Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew. Extreme learning machine: Theory and applications [J]. Neurocomputing. (2006)70: 489–501.

DOI: 10.1109/ijcnn.2004.1380068

Google Scholar

[5] Hans Hersbach. CMOD5-An improved geophysical model function for ERS C-band scatterometry[R]. European Centre for Medium Range Weather Forecasts. 2003: 47.

Google Scholar

[6] Anton Verhoef, Marcos Portabella and Ad Stoffelen. CMOD5. n - the CMOD5 GMF for neutral winds [T]. Ocean and Sea Ice SAF. 2008: 13.

Google Scholar