人民黄河2024，Vol.46Issue(4)：43-48,6.DOI:10.3969/j.issn.1000-1379.2024.04.007
基于图注意力网络的城市内涝积水预测与研究
Prediction and Research of Urban Waterlogging Based on Graph Attention Network
摘要
Abstract
The frequent occurrence of extreme heavy rainfall in cities has posed a severe threat to the personal and property safety of residents due to urban flooding.Accurate and efficient prediction of flooding areas within cities plays a crucial role in enhancing urban disaster emer-gency response capabilities.In order to improve the accuracy and intuitiveness of urban flooding area predictions,this article proposed a com-bination prediction model called GATLSTM,based on GAT(Graph Attention Network)and LSTM(Long Short-Time Memory).The GAT was used to extract local spatial features of flooding information,and it enhanced the memory of key information sequences by assigning weights to nodes.Subsequently,LSTM was employed to extract temporal features of flooding area sequences and predicted the flooding areas at inundation points for the next 10 minutes.The model was built and evaluated by using inundation data from a specific point in Kaifeng City.It was compared with LSTM,GAT and GCNLSTM models.The results indicate that the GATLSTM model outperforms the other three models in terms of prediction accuracy.It can accurately forecast flooding areas at inundation points in the short term,providing a scientific basis for flood prevention efforts and emergency response measures.关键词
积水预测/城市暴雨/图注意力网络/长短期记忆网络Key words
waterlogging forecast/urban rainstorm/Graph Attention Network/Long Short-Term Memory分类
信息技术与安全科学引用本文复制引用
胡昊,孙爽,马鑫,李擎,徐鹏..基于图注意力网络的城市内涝积水预测与研究[J].人民黄河,2024,46(4):43-48,6.基金项目
河南省重大科技专项(221100320200,231100320100) （221100320200,231100320100）
河南省高等学校青年骨干教师培养计划项目(2019GCJS105) （2019GCJS105）
开封市重点研发专项(22ZDYF007) （22ZDYF007）
