基于图注意力网络的城市内涝积水预测与研究.

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 emergency response capabilities. In order to improve the accuracy and intuitiveness of urban flooding area predictions, this article proposed a combination 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. [ABSTRACT FROM AUTHOR]