Quickly forecasting the future state of urban sensors by the missing-data-tolerant deep learning approach
文献类型：期刊论文
| 作者 | Wang, Peixiao2,3,4; Zhang, Hengcai2,3; Cheng, Shifen2,3; Zhang, Tong4; Lu, Feng1,2,3 |
| 刊名 | SUSTAINABLE CITIES AND SOCIETY
 |
| 出版日期 | 2025 |
| 卷号 | 118页码:106044 |
| 关键词 | Spatiotemporal prediction Spatiotemporal data missing Causal dilatation convolution Graph attention network |
| ISSN号 | 2210-6707 |
| DOI | 10.1016/j.scs.2024.106044 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurately and quickly forecasting the future state of urban sensors is crucial for urban monitoring and management. Although many forecasting approaches have been proposed, existing models still face two major challenges. First, most approaches do not have the ability to automatically handle missing data. Second, most approaches have high complexity, neglecting the usability and lightweight of the approach. Therefore, we present a lightweight spatiotemporal dilation approach tolerating missing data (STDM) to address the aforementioned challenges. First, we integrate a missing data handling mechanism into the STDM approach to enhance its forecasting capability under missing scenarios. Second, we present a lightweight spatiotemporal dilation component to enhance the inference speed of the STDM approach. Finally, we design the STDM approach as a separable architecture and define a corresponding loss function, allowing the STDM approach to be compatible with both forecasting tasks under missing and non-missing scenarios. The approach underwent validation using traffic, PM2.5, and temperature datasets. It exhibited superior forecasting accuracy and inference speed across four missing scenarios, outperforming eight baselines. Codes and data are available at link on https ://doi.org/10.6084/m9.figshare.24289456. |
| URL标识 | 查看原文 |
| WOS关键词 | NETWORKS ; MODEL |
| WOS研究方向 | Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001388719200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211265]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Hengcai |
| 作者单位 | 1.Fujian Collaborat Innovat Ctr Big Data Applicat Go, Fuzhou 350003, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,et al. Quickly forecasting the future state of urban sensors by the missing-data-tolerant deep learning approach[J]. SUSTAINABLE CITIES AND SOCIETY,2025,118:106044. |
| APA | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,Zhang, Tong,&Lu, Feng.(2025).Quickly forecasting the future state of urban sensors by the missing-data-tolerant deep learning approach.SUSTAINABLE CITIES AND SOCIETY,118,106044. |
| MLA | Wang, Peixiao,et al."Quickly forecasting the future state of urban sensors by the missing-data-tolerant deep learning approach".SUSTAINABLE CITIES AND SOCIETY 118(2025):106044. |
入库方式： OAI收割
来源：地理科学与资源研究所
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