A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction
文献类型：期刊论文
| 作者 | Wang, Peixiao1; Zhang, Hengcai1; Cheng, Shifen1; Zhang, Tong2; Lu, Feng1,4; Wu, Sheng3,4 |
| 刊名 | SUSTAINABLE CITIES AND SOCIETY
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 101页码:105105 |
| 关键词 | Urban computing Causal dilated convolution Graph dilated convolution Spatiotemporal prediction |
| DOI | 10.1016/j.scs.2023.105105 |
| 产权排序 | 1 |
| 英文摘要 | Spatiotemporal prediction is one attractive research topic in urban computing, which is of great significance to urban planning and management. At present, there are many attempts to predict the spatiotemporal state of systems using various deep learning models. However, most existing models tend to improve prediction accuracy with larger parameter scale and time consumption, but ignoring ease of use in practice. To overcome this question, we propose a lightweight spatiotemporal graph dilated convolutional network called STGDN with satisfactory prediction accuracy and lower model complexity. More specifically, we propose a novel dilated convolution operator and integrate it into traditional causal convolutional networks and graph convolutional networks to greatly improve the efficiency of prediction. The proposed dilated convolution operator can significantly reduce the depth of the model, thereby reducing the parameter scale and improving the computational efficiency of the model. We conducted on multi experiments on three real-world spatiotemporal datasets (traffic dataset, PM2.5 dataset, and temperature dataset) to prove the effectiveness and advantage of our proposed STGDN. The experimental results show that the proposed STGDN model outperforms or achieves comparable prediction accuracy of the existing nine baselines with higher operational efficiency and fewer model parameters. Codes are available at anonymous private link on https://doi.org/10.6084/m9.figshare.23935683. |
| WOS研究方向 | Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels |
| WOS记录号 | WOS:001139647400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201662]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430079, Peoples R China 4.Fuzhou Univ, Acad Digital China Fujian, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350002, Peoples R China 5.Fuzhou Univ, Ctr Discrete Math, Fuzhou 350003, Fujian, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,et al. A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction[J]. SUSTAINABLE CITIES AND SOCIETY,2024,101:105105. |
| APA | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,Zhang, Tong,Lu, Feng,&Wu, Sheng.(2024).A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction.SUSTAINABLE CITIES AND SOCIETY,101,105105. |
| MLA | Wang, Peixiao,et al."A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction".SUSTAINABLE CITIES AND SOCIETY 101(2024):105105. |
入库方式： OAI收割
来源：地理科学与资源研究所
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