Improved rainfield tracking using radial basis functions

Fabio Dell'Acqua; Paolo Gamba

doi:10.1117/12.454143

28 January 2002 Improved rainfield tracking using radial basis functions

Fabio Dell'Acqua, Paolo Gamba

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454143
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

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.

Citation Download Citation

Fabio Dell'Acqua and Paolo Gamba "Improved rainfield tracking using radial basis functions", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454143

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