A hybrid neural network system for the rainfall estimation using satellite imagery

Hybrid neural networks composed of a self-organizing map (SOM) and three-layered feedforward neural networks have been developed and applied for rainfall estimation using satellite imagery. The SOM classifies an input vector extracted from satellite imagery, then one of the feedforward neural networks is chosen according to the class to give the rainfall estimation. In order to train the hybrid neural network, adjoining seas of Japan were selected as testing area. Hourly GMS infrared imagery data and simultaneous ground truth data (the area average of rainfall observations and radar/raingage composite data) were collected from AIP/l² data sets. The SOM is trained to classify the textural feature vectors extracted from the imagery data, and tuned by learning vector quantization method. The feedforward neural networks are trained to give the estimation by back propagation algorithm. Fairly good correlation coefficients about 0.8 are obtained between the estimation and corresponding ground truth for the unlearned test set. Furthermore, SOM with a recurrent structure for processing the temporal information has been proposed and tested.