Knowledge Commons of Institute of Automation,CAS
Multi-modal spatio-temporal meteorological forecasting with deep neural network | |
Xinbang Zhang1,2; Qizhao Jin1,2; Tingzhao Yu3; Shiming Xiang1,2; Qiuming Kuang3; Véronique Prinet1; Chunhong Pan1 | |
发表期刊 | ISPRS Journal of Photogrammetry and Remote Sensing |
2022-03 | |
页码 | 14 |
文章类型 | 已录用未发表 |
摘要 | Meteorological forecasting is a typical and fundamental problem in the remote sensing field. Although many brilliant forecasting methods have been developed, long-term (a few days ahead) meteorological prediction still relies on traditional Numerical Weather Prediction (NWP) that is not competent for the oncoming flood of meteorological data. To improve the forecasting ability faced with meteorological big data, this article adopts the Automated Machine Learning (AutoML) technique and proposes a deep learning framework to model the dynamics of multi-modal meteorological data along spatial and temporal dimensions. Spatially, a convolution based network is developed to extract the spatial context of multi-modal meteorological data. Considering the complex relationship between different modalities, the Neural Architecture Search (NAS) technique is introduced to automate the designing procedure of the fusion network in a purely data-driven manner. As for the temporal dimension, an encoder-decoder structure is built to exhaustively model the temporal dynamics of the embedding sequence. Specializing for the numerical sequence representation transformation, the multi-head attention module endows the proposed model with the ability to forecast future data. Generally speaking, the whole framework could be optimized with the standard back-propagation, yielding an end-to-end learning mechanism. To investigate its feasibility, the proposed model is evaluated with four typical meteorological modalities including temperature, relative humidity, and two components of wind, which are all restricted under the region whose latitude and longitude range from to N and E to E, respectively. Experiments on two datasets with different resolutions verify that deep learning is effective as an operational technique for the meteorological forecasting task. |
关键词 | Meterological forecasting Deep learning Neural architecture search AutoML |
收录类别 | SCI |
语种 | 英语 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48955 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Shiming Xiang |
作者单位 | 1.The Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.The School of Artificial Intelligence, University of Chinese Academy of Sciences 3.The Public Meteorological Service Center, China Meteorological Administration |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xinbang Zhang,Qizhao Jin,Tingzhao Yu,et al. Multi-modal spatio-temporal meteorological forecasting with deep neural network[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2022:14. |
APA | Xinbang Zhang.,Qizhao Jin.,Tingzhao Yu.,Shiming Xiang.,Qiuming Kuang.,...&Chunhong Pan.(2022).Multi-modal spatio-temporal meteorological forecasting with deep neural network.ISPRS Journal of Photogrammetry and Remote Sensing,14. |
MLA | Xinbang Zhang,et al."Multi-modal spatio-temporal meteorological forecasting with deep neural network".ISPRS Journal of Photogrammetry and Remote Sensing (2022):14. |
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