Precipitation data merging using artificial neural networks

Qian Du

doi:10.1117/12.818376

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19 March 2009 Precipitation data merging using artificial neural networks

Qian Du

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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.

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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

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