Urban Flood Disaster Control Based on Green-gray-blue Infrastructure Integration

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Urban Flood Disaster Control Based on Green-gray-blue Infrastructure Integration

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[1]HUANG Guoru,YANG Ge,ZENG Bowei,et al.Urban Flood Disaster Control Based on Green-gray-blue Infrastructure Integration[J].Journal of Zhengzhou University (Engineering Science),2023,44(02):68-74.[doi:10.13705/j.issn.1671-6833.2023.02.012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 02 Page number: 68-74 Column: Public date: 2023-02-27

Title:: Urban Flood Disaster Control Based on Green-gray-blue Infrastructure Integration

Author(s):: HUANG Guoru¹; 2; YANG Ge¹; 2; ZENG Bowei¹; LYU Yongpeng¹; 2; REN Xinxin³; 1.School of Computer Science and Engineering, Northern Nationalities University, Yinchuan 750021 in Ningxia, Key Laboratory of the National Committee of the National Committee of the University of Nationalities, Ningxia Yinchuan 750021, 2.School of Computer Science and Engineering, Northern National University, Ningxia Yinchuan 750021, 3.Northern National University Business School, Ningxia Yinchuan, Ningxia Yinchuan, Ningxia 750021

Keywords:: signed network; graph neural network; graph attention networks; network embedding; link prediction

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2023.02.012

Abstract:: Network node embedding is mapping nodes in a network to a low-dimensional vector representation, so that vector space-based learning methods can be directly applied to handle downstream tasks such as link prediction. Most of the existing network node embedding models were for unsigned networks and could not be directly used to deal with signed networks (usually need to be converted into unsigned networks for processing, thus discarding a lot of valuable information embedded in the positive and negative signs on the edges).A node embedding model (SNEDA)based on graphical neural networks was proposed that could directly deal with symbolic networks. Based on structural balance theory and status theory, the paths between nodes were divided into 20 different motif structures according to the direction and the positive and negative information on the edges. A network propagation model was designed with two levels of attention mechanism, which could capture the contribution and influence of different neighboring nodes to the vector representation of node i by node-level attention mechanism when aggregating the direct neighboring information of node i, and captured the vector representation of different motif to node i by path-level attention when aggregating the second-order and higher-order neighboring information of node i. A two-level attention mechanism was introduced to integrate different contributions and influences at the node level and path level, which could it not only improve the time efficiency of the algorithm but also make the final vector representation of node i more beneficial to improve the prediction accuracy of the downstream link prediction task. Through experiments conducted on four real social network datasets, the SNEDA model improved the AUC and F1 metrics by about 3.1% and 1.1%, respectively, compared with the benchmark model, and the results showed that the network representation obtained by the model could improve the accuracy of link prediction.

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Last Update: 2023-02-25