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19 March 2009 Precipitation data merging using artificial neural networks
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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