## 1 Introduction  
Effective flood forecasting methods contribute to flood control and disaster emergency management [1]. From the perspective of data analysis, flood forecasting based on data-driven models is essentially time-series data prediction [2]. At present, flood time-series data prediction research based on the data driven model mainly faces the following challenges [3]. The inherent nonlinearity of the flood process and the uncertainty of the predicted results bring risks to flood control decision-making. The DL model is only trained from available datasets and cannot reasonably utilize scientific principles or laws during the model training process, which may lead to unreasonable predictions. In the actual measurement, the quality of the collected data cannot be guaranteed, and the data may have noise or outliers. Due to the data-driven nature of the learning process, the robustness and versatility of the learning results are poor, resulting in completely incorrect results [4]. Therefore, many researchers have proposed using data-driven and knowledge-guided models to address the aforementioned challenges [5]. The effective implementation of the above data-driven and knowledge-guided prediction theory relies on machine learning prediction models. Among these, depth neural networks are one of the most representative machine learning methods. Flood prediction methods of deep neural networks mainly include RNN-based prediction methods [6],LSTM-based prediction methods [7], and GRU-based prediction methods [8]. The above intelligent methods mainly start from the perspective of Euclidean data regression, ignoring the important physical structure of the basin itself and the spatiotemporal characteristics of data features. Accurate flood prediction needs to be based on the physical structure of the watershed itself and the spatiotemporal characteristics of the data [9]. The prediction method based on the graph neural network model [10] extends the deep learning model to non-Euclidean space, and can extract the spatiotemporal characteristics of the flood physical field in parallel. It fully considers the physical structure within the watershed, namely, various spatial topological connections. At present, research on flood forecasting methods based on graph neural networks mainly focuses on isomorphic and heterogeneous graphs [11]. Among these, isomorphic graphs only have one type of node and edge, which corresponds to the field of flood prediction, that is, only one type of station and its runoff direction relationship are predicted, which clearly does not comply with the physical mechanism of flood basins. Considering the spatiotemporal complexity of flood processes, modeling them as heterogeneous maps containing multiple types of nodes and edges can achieve more detailed and comprehensive flood processes. By combining the different attribute features of different hydrological stations to extract a wide range of spatiotemporal features, more accurate flood spatiotemporal prediction can be achieved [12]. Recurrent neural networks are used to solve the problem of training sample inputs as continuous sequences with different sequence lengths. However, it has short-term memory problems, cannot handle long input sequences, and is prone to gradient explosion or gradient disappearance [13]. The gradient problem of Recurrent Neural Network (RNN) has been solved for a certain gradient problem in Long Short-Term Memory (LSTM), but the requirements for data must be time-dependent. In addition, LSTM is unable to perform parallel operations and has very high hardware requirements during the training process. Temporal Convolutional Network (TCN) can run in parallel. Their flexible receptive field and stable gradient are more suitable for capturing the characteristics of long time-series data [14]. In addition, the convolutional core of the TCN network is shared in one layer and the memory usage during training is low [15].