DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Jinliang Yao, Junwei Ji (), Rongbo Wang, Xiaoxi Huang, Zhiming Kang () and Xiaoran Zhuang
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Jinliang Yao: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Junwei Ji: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Rongbo Wang: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Xiaoxi Huang: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Zhiming Kang: Jiangsu Meteorological Observatory, Nanjing 210008, China
Xiaoran Zhuang: Jiangsu Meteorological Observatory, Nanjing 210008, China

Sustainability, 2024, vol. 16, issue 9, 1-16

Abstract: Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

Keywords: precipitation nowcasting; DSADNet; fusion module; dynamic convolution; attention mechanism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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