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Two-stream convolutional LSTM for precipitation nowcasting
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Journal Article
Two-stream convolutional LSTM for precipitation nowcasting
2022
Overview
Reliable precipitation nowcasting is essential to many fields, which can guide people to reasonably carry out production activities and respond to rainstorm disasters. However, precipitation nowcasting is a very challenging task because of correlation and heterogeneity both in space and in time. Most previous studies have not adequately captured the long-term and long-range spatiotemporal dependencies in the data, leading to insufficient modeling and poor prediction performance. To make more accurate prediction, we propose a novel deep learning model for precipitation nowcasting, called two-stream convolutional LSTM which includes short-term sub-network and long-term sub-network. The two sub-networks, respectively, make predictions on inputs at different time intervals to capture the heterogeneity of rainfall data. On this basis, an innovative recombination module is proposed to fuse the outputs of two sub-networks. In addition, we embed the 3D convolutions and self-attention mechanism to construct a new memory cell, named 3D-SA-LSTM, to extract the spatiotemporal feature. Two-stream convolutional LSTM achieves the state-of-the-art prediction performance on a real-world large-scale dataset and is a more flexible framework that can be conveniently applied to other similarly time series prediction tasks: traffic forecasting and planning, financial analysis and management, actions recognition and prediction, etc.
Publisher
Springer London,Springer Nature B.V
Subject
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Probability and Statistics in Computer Science
/ Rain
/ Rainfall
/ S.I. : Deep Learning for Time Series Data
