标题: Integrating domain knowledge and graph convolutional neural networks to support river network selection

作者: Yu, HF (Yu, Huafei); Ai, TH (Ai, Tinghua); Yang, M (Yang, Min); Li, JZ (Li, Jingzhong); Wang, L (Wang, Lu); Gao, A (Gao, Aji); Xiao, TY (Xiao, Tianyuan); Zhou, Z (Zhou, Zhe)

来源出版物: TRANSACTIONS IN GIS DOI: 10.1111/tgis.13104 提前访问日期: OCT 2023

摘要: Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine-tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.

地址: [Yu, Huafei; Ai, Tinghua; Yang, Min; Li, Jingzhong; Wang, Lu; Gao, Aji; Xiao, Tianyuan; Zhou, Zhe] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Wang, Lu] Jinan Surveying & Mapping Res Inst, Jinan, Peoples R China.

[Ai, Tinghua] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址: Ai, TH (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: tinghuaai@whu.edu.cn

影响因子：2.4