无线电工程2024，Vol.54Issue(1)：47-54,8.DOI:10.3969/j.issn.1003-3106.2024.01.007
基于UI-LSTM模型的短时降水预测研究
Research on Short-term Precipitation Prediction Based on UI-LSTM Model
包顺 1秦华旺 1戴跃伟 1陈浩然 2尹传豪1
作者信息
- 1. 南京信息工程大学电子与信息工程学院,江苏南京 210044
- 2. 南京信息工程大学自动化学院,江苏南京 210044
- 折叠
摘要
Abstract
Precipitation nowcasting is to predict short-term rainfall in the future.Most existing precipitation forecasting models based on Recurrent Neural Network(RNN)use a single convolution kernel to extract the features of the input and hidden states,which is limited by locality.Thus these models cannot capture complex physical changes in radar echo images,and cannot effectively extract spatiotemporal correlations and make accurate forecasts for heavy rainfall regions.In view of the problems in the existing models,the UI-LSTM model is proposed for precipitation nowcasting,which can effectively extract the spatiotemporal correlation of the radar echo sequence.The proposed model adopts a U-shaped structure and uses skip connections for feature stitching to learn the contextual semantic information of the entire radar echo map and fuse features from the shallow and deep information.In addition,the Inception structure is added to replace the convolution in the ConvLSTM cell structure,and features of the input and the hidden state are effectively extracted through convolution kernels of different sizes.The experimental results show that the UI-LSTM model performs much better than the existing model in terms of HSS,CSI,MAE and SSIM,and the accuracy of heavy precipitation prediction is improved.关键词
降水临近预报/循环神经网络/特征融合/UI-LSTM/InceptionKey words
precipitation nowcasting/RNN/feature fusion/UI-LSTM/Inception分类
计算机与自动化引用本文复制引用
包顺,秦华旺,戴跃伟,陈浩然,尹传豪..基于UI-LSTM模型的短时降水预测研究[J].无线电工程,2024,54(1):47-54,8.
