Precipitation data merging using artificial neural networks

Qian Du

doi:10.1117/12.818376

19 March 2009 Precipitation data merging using artificial neural networks

Qian Du

Author Affiliations +

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 73430E (2009) https://doi.org/10.1117/12.818376
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Qian Du "Precipitation data merging using artificial neural networks", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430E (19 March 2009); https://doi.org/10.1117/12.818376

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE