With the continuous intensification of global climate change and the rapid urbanization,urban waterlogging disasters caused by extreme rainfall events have become increasingly severe,posing a serious challenge for many cities around the world.Here,we propose a deep learning approach to predict urban waterlogging depth,which is based on Long Short-Term Memory (LSTM) and rainfall data from May to August 2021 measured by 75 national automatic meteorological observation stations in Zhejiang's Zhuji city and the water depth data of a typical waterlogging site.The relationship between rainfall and waterlogging depth constructed by LSTM provides the next 2-hour urban waterlogging depth forecast with an interval of 15 minutes.When compared with Random Forest (RF) and Artificial Neural Network (ANN) models,the proposed LSTM approach,using water depth and precipitation data over the past 4 hours to predict the next 2-hour waterlogging depth,demonstrates the best performance by lower root mean square error (<5.6 cm),higher correlation coefficient (>0.93) and Nash-Sutcliffe efficiency coefficient (>0.86).It can be concluded that the proposed deep learning approach is feasible and applicable for urban waterlogging depth prediction.