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Proceedings Paper

Improved rainfield tracking using radial basis functions
Author(s): Fabio Dell'Acqua; Paolo Gamba
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Paper Abstract

Many approaches to short-term forecasting the motion of rain structures widely rely on correlation between radar rain maps using local rain intensities. A different approach can be taken in considering rain structures as the base for analysis, while local rain intensities only serve the purpose of detecting, locating and shaping the former. RBF (equalsRadial Basis Function) Neural Networks (NN) provide a means of implementing such approach. Rain maps submitted to RBF NN for training results in turning them into sets of parameters describing observed rain structures. Reiterating the training on time series of maps results in time series of parameters possibly depicting typical trends. Forecasting such parameters and translating forecasted values back into maps should provide a forecast of rain distribution in the near future. We found the best forecasting strategy to be a mix where some of the parameters are forecasted linearly and some else using more RBF NNs. We got further improvement by using GRBF (equalsGradient RBF) in place of RBF in forecasting phase, and making the synthesis phase more stable and reliable by introducing some novelties into the algorithm. In this paper we explain the technique we developed and evaluate the results we obtained.

Paper Details

Date Published: 28 January 2002
PDF: 12 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454143
Show Author Affiliations
Fabio Dell'Acqua, Univ. degli Studi di Pavia (Italy)
Paolo Gamba, Univ. degli Studi di Pavia (Italy)


Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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