A meteorological disaster prediction model based on U-Net

Meteorological disasters suffered from problems of multiple, frequent, and variable forms, as well as increasing and sudden extreme weather events. Thus, we propose an U-shape model based on Resnet. Firstly, we put the last 24 hours’ precipitation radar map with a dimension of 240 as the input to predict the respective meteorological disaster probabilities of each pixel. A backbone based on Resnet34 model is used to extract the features of the compress the temporal dimension, the encoder and decoder with U-shape are constructed using the previously defined down and up classes. Finally, the output image as the disaster prediction result. To demonstrate the efficiency of the proposed algorithm, we collected a set of precipitation radar data and meteorological disaster warning data for the corresponding time for model training and testing. The experimental results compared with the conventional CNN backbone for this dataset proved that the proposed algorithm can realize regional meteorological disaster prediction with SSIM of 0.92, which to a certain extent provides a reference for subsequent research.