Lightweighting the prediction process of urban states with parameter sharing and dilated operations
文献类型：期刊论文
| 作者 | Wang, Peixiao3,4; Yang, Haolong2; Zhang, Hengcai3,4; Cheng, Shifen3,4; Lu, Feng3,4; Chen, Zeqiang1 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:2468414 |
| 关键词 | Urban states spatio-temporal prediction dilated operation parameter sharing hyper-parameter dependence |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2468414 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Lightweight and high-precision prediction models for urban states are anticipated to run efficiently on resource-limited devices, serving as key technologies for realizing smart city management. However, many existing models, despite achieving high prediction precision, suffer from overly complex designs, leading to low computational efficiency, a large number of learnable parameters, and difficulty in hyper-parameter calibration. In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. Specifically, we define parameter-shared temporal/graph dilated convolution operators to efficiently and accurately capture spatio-temporal correlations without significantly increasing model's computation time and scale of learnable parameters. Furthermore, we establish mathematical relationships between hyperparameters, significantly reducing their number and simplifying the calibration process. The PSDCN model was validated using PM2.5, traffic, and temperature datasets. The results demonstrated that the PSDCN model simplifies hyperparameter calibration. It also either outperforms or matches the prediction accuracy of nine baselines, while achieving better time efficiency and requiring fewer learnable parameters. |
| URL标识 | 查看原文 |
| WOS关键词 | OPTIMIZATION ; NETWORK |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001425412200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212270]  |
| 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.China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China 2.Concordia Univ, Gina Cody Sch Engn & Comp Sci, Montreal, PQ, Canada; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Peixiao,Yang, Haolong,Zhang, Hengcai,et al. Lightweighting the prediction process of urban states with parameter sharing and dilated operations[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):2468414. |
| APA | Wang, Peixiao,Yang, Haolong,Zhang, Hengcai,Cheng, Shifen,Lu, Feng,&Chen, Zeqiang.(2025).Lightweighting the prediction process of urban states with parameter sharing and dilated operations.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),2468414. |
| MLA | Wang, Peixiao,et al."Lightweighting the prediction process of urban states with parameter sharing and dilated operations".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):2468414. |
入库方式： OAI收割
来源：地理科学与资源研究所
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