## 1 Introduction  
With climate warming and accelerated urbanization, the frequency of extreme rainfall events has increased. Rainstorm flood has become one of the main natural disasters plaguing many cities in China, and has brought serious threats to human production, life and socio-economic activities [1, 2, 3]. From 16 to 21 July 2021, a rare and continuous heavy precipitation weather process occurred in Zhengzhou, and, as of 12:00 on August 2, the heavy flooding caused by heavy rain caused direct economic losses of CNY 114.269 billion, 302 deaths and 50 people missing [4, 5, 6]. As one of the important non-engineering measures for flood control and disaster reduction, waterlogging prediction plays a key supporting role in urban flood control and disaster reduction. Although the existing physically driven urban waterlogging model can reflect the characteristics of urban rainfall-ponding in a certain period, there are still problems such as inefficient calculation and complex modeling [7]. With the advent of the era of big data and artificial intelligence, the abundance of hydrological observation data and the improvement of computer computing power [8], the use of neural networks for deep learning in the computer field to solve the problem of waterlogging prediction has become the focus of attention [9; 10; 11]. Among many neural networks, recurrent neural network (RNN) has better performance in processing and predicting time series data [12], but it also has the problem of gradient disappearance or explosion, which easily leads to unsatisfactory prediction results [13]. The LSTM is a variant of RNN, which largely improves the ability to handle long-term dependence problems and can handle time-dependent data well [14]. At present, the LSTM method is being widely used in multivariable field of hydrology. [PERSON] et al. [15] used the LSTM model to study the water level fluctuation and reservoir operation in Dongting Lake. Comparing the LSTM model with the SVM model and comprehensively examining the impact of the presence of the Three Gorges Dam on the water level of Dongting Lake, it was concluded that the deviation value of the LSTM model was much smaller than that of the SVM model and showed better results in the prediction of high water levels; [PERSON] et al. [16] proposed a deep learning algorithm based on a recursive long-term and short-term memory neural network to predict the irrigation flow in the dry season in advance. By comparing the measured data of basin stations, the results show that the relative error is less than 10%. [PERSON] et al. [17] proposed a lake water level prediction model based on FFNN and LSTM, performed an error analysis of the model simulation effects and proved that the model was effective in predicting time series data. [PERSON] et al. [18] adopted a deep neural network composed of a convolutional neural network and an LSTM recursive module. The network model improved the resolution and accuracy of the GCM precipitation prediction. [PERSON] et al. [19] trained LSTM models using a large flow dataset and compared the LSTM model prediction results with the actual flows, and found that the LSTM model can be used to predict watershed flows [20]. [PERSON] et al. [21] discussed the applicability of LSTM in water level prediction in urban drainage pipelines. [PERSON] et al. [22] compared the ability of LSTM and ANN in rainfall runoff, and the results showed that the prediction accuracy of LSTM was higher than that of ANN. Combined with the research status, machine learning technology is mostly applied to river basins, and its application in urban hydrology needs to be strengthened. However, the LSTM method itself is very mature, and the interdisciplinary research of machine learning technology represented by LSTM and the waterlogging simulation field is worth extensive testing. Therefore, the Jianye District of Nanjing, located in southeast China, is taken as the study area.