Rainfall spatiotemporal interpolation method based on CNN-GRU with attention
Authors: 
Qiang Hu, Qi Sun, Zhi-Hao Chen, He-Xuan Hu, Ye Zhang, Zhen-Yun HuAuthors Info & Claims
Qiang Hu, Qi Sun, Zhi-Hao Chen, He-Xuan Hu, Ye Zhang, Zhen-Yun HuAuthors Info & ClaimsArticle No.: 73, Pages 1 - 9
Abstract
The meteorological and hydrological data required for rainfall-runoff forecasting often rely on the observation data from existing surface weather stations, but these data cannot fully reflect the overall space-time characteristics of the watershed. In order to solve the problem of lacking hydrological spatiotemporal data samples, this paper proposes a rainfall spatiotemporal interpolation method based on CNN-GRU with attention, which applies convolutional neural network (CNN) to fuse the spatial characteristics of multi-source data, and introduces gated recurrent unit (GRU) and attention to capture temporal characteristics and important historical information. Utilizing the characteristics of neural networks that can effectively fit complex and nonlinear spatiotemporal processes, capture the residual relationship between actual rainfall data and satellite remote sensing rainfall data, surrounding weather station data, elevation data, and train spatial distribution prediction models. The proposed spatiotemporal interpolation algorithm for rainfall can effectively fuse the different characteristics of multi-source data and ultimately generate high-precision, areal estimated raster rainfall data.
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May 2023
711 pages
ISBN:9798400708237
DOI:10.1145/3604078
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Published: 26 October 2023
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ICDIP 2023
ICDIP 2023: The 15th International Conference on Digital Image Processing
May 19 - 22, 2023
Nanjing, China
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