PURPOSE: To shorten the time of learning for a radar image which is newly measured and to find radar images at irregular forecasting time intervals with a small calculation quantity by systematically classifying and managing weather radar images and utilizing the weight of a neural network model which has learnt by using radar images by clusters.
CONSTITUTION: Radar images from an input part 100 are classified by patterns by using the neural network for pattern classification (201). The weight of the neural network model which is generated in the past with a corresponding cluster and used by a learning and forecasting part 203 is obtained and set as an initial value for relearning. Further, optimum weight is generated for each pattern-classified cluster and registered in a data base part 202 so that it can be used. Further, an index for indicating forecasting time is inputted to the neural network model of the learning and forecasting part 203 together with the radar image.
SONEHARA NOBORU
SUZUKI HIDETO
