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Proceedings Paper

Precipitation data merging using artificial neural networks
Author(s): Qian Du
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Paper Abstract

Precipitation is an important parameter in hydrologic and climate research. In addition to gauge and radar observations, there are several satellites providing spaceborne observations. Effectively merging these data products can improve the rainfall estimation accuracy. In this paper, we investigate the use of neural networks, i.e., multi-layer backpropagation neural network and radial basis function neural network in precipitation data merging. We also investigate the performance improvement from training sample selection via principal component analysis. The preliminary results show that our data merging approaches can outperform other linear methods such as weighted sum and the data preprocessing can also improve the performance.

Paper Details

Date Published: 19 March 2009
PDF: 8 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430E (19 March 2009); doi: 10.1117/12.818376
Show Author Affiliations
Qian Du, Mississippi State Univ. (United States)


Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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