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SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion | |
Jin, Qizhao1,2![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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ISSN | 1939-1404 |
2024 | |
卷号 | 17页码:1299-1314 |
产权排序 | 1 |
摘要 | Precipitation plays a significant role in global water and energy cycles, largely affecting many aspects of human life, such as transportation and agriculture. Recently, meteorologists have tried to predict precipitation with deep learning methods by learning from much historical meteorological data. Under this paradigm, the task of precipitation nowcasting is formulated as a spatiotemporal sequence forecasting problem. However, current studies suffer from two inherent drawbacks of the definition of the problem. First, considering that the weather patterns vary in spatial and temporal dimensions, a spatiotemporally shared kernel is not optimal for capturing features across different regions and seasons. Second, these methods isolate the precipitation from other meteorological elements, such as temperature, humidity, and wind. The disability of cross-model learning prevents the possibility of the promotion of precipitation prediction. Therefore, this article proposes a spatiotemporal inference network (STIN) to produce precipitation prediction from multimodal meteorological data with spatiotemporal specific filters. Specifically, we first design a spatiotemporal-aware convolutional layer (STAConv), in which kernels are generated conditioned on the incoming spatiotemporally features vector. Replacing normal convolution with STAConv enables the extraction of spatiotemporal specific information from the meteorological data. Based on the STAConv, the spatiotemporal-aware convolutional neural network (STACNN) is further proposed, fusing the multimodal information, including temperature, humidity, and wind. Then, an encoder-decoder framework composed of RNN layers is built to extract representative temporal dynamics from multimodal information. To investigate the practicality of the proposed method, we employ STIN to predict the following precipitation intensity. Extensive experiments on three meteorological datasets demonstrate the effectiveness of our model on precipitation nowcasting. |
关键词 | Data mining multimodal knowledge discovery precipitation nowcasting |
学科领域 | 模式识别 |
DOI | 10.1109/JSTARS.2023.3321963 |
关键词[WOS] | PRODUCTS ; IMERG |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:001127459900015 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+科学 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54839 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Xiao, Xinyu |
作者单位 | 1.University of Chinese Academy of Sciences, School of Artificial Intelligence, Beijing 100049, People's Republic of China 2.Chinese Academy of Sciences, Institute of Automation, National Laboratory of Pattern Recognition, Beijing 100190, People's Republic of China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jin, Qizhao,Zhang, Xinbang,Xiao, Xinyu,et al. SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:1299-1314. |
APA | Jin, Qizhao.,Zhang, Xinbang.,Xiao, Xinyu.,Wang, Ying.,Meng, Gaofeng.,...&Pan, Chunhong.(2024).SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,1299-1314. |
MLA | Jin, Qizhao,et al."SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):1299-1314. |
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