## 1 Introduction  
With the shortage of water resources and rapid urbanization, the realization of fine management with high efficiency of water supply services has become the primary problem to be solved in smart water. As the foundation of smart water [1], water demand forecasting plays an important role in water supply services. More specifically, accurate water demand forecasts can not only guide the water allocation but also provide data support for water leakage detection. However, water demand is usually characterized by complex nonlinearities, influenced by numerous factors. Therefore, how to make accurate water demand forecasting is a very challenging issue.  
In the field of water demand forecasting, many methods have been proposed. These methods can be categorized into statistical and machine learning methods based on different principles. In early studies, statistical methods were widely used in water demand forecasting because of their simplicity and good interpretability. General models are autoregressive integrated moving average (ARIMA) [2] and vector autoregressive (VAR) [3]. However, changes in water demand exhibit intricate nonlinear patterns, which make it challenging for statistical methods based on linear assumptions to accurately predict water demand. To address this issue, researchers applied conventional machine learning methods to water demand forecasting. Two representative methods are random forest (RF) [4] and support vector machine (SVM) [5]. Although these methods can describe non-linear relationships, they typically rely on complex feature engineering. It is difficult to achieve accurate prediction when key features affecting water demand changes cannot be effectively extracted. The advantages of deep learning methods, such as automatic feature learning and representation, have made them increasingly the preferred choice for researchers in water demand forecasting. The utilization of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) generally outperform traditional forecasting methods in predicting water demand [6]. However, these methods mainly focus on the analysis of temporal autocorrelation and cannot fully exploit the rich water demand monitoring data. In order to further improve the predictive accuracy of models, some researchers have begun to incorporate spatial correlation by using a combination of CNN and RNN to extract spatio-temporal features from data [7]. However, the limitation of the method is that it can only handle standard grid data.  
In this paper, we propose a Dynamic Graph convolution based Spatio-Temporal Feature Network (DG-STFN) to jointly predict the water demand of multiple residential neighborhoods in a region. Our model does not have any specific limitations on data inputs and can efficiently capture the spatial and temporal features of water demand monitoring data. The main contributions of our work are as follows.  
(1) We design a Dynamic Graph Generation module (DG) based on an attention mechanism, which explores the global dependencies and local dynamic changes in water demand among multiple residential neighborhoods in a fully data-driven manner.  
(2) We build a Spatial-Temporal Feature Network (STFN) for water demand forecasting based on graph convolutional networks (GCN) and one-dimension convolutional neural networks (1D-CNN).  
(3) We conduct comparative experiments on two real-world datasets and the results show that DG-STFN outperforms the baseline methods.