The real-time hydraulic model (RTHM) is a key assistive tool in water distribution system (WDS) management, and its performance directly affects assisted decision-making. This study develops a framework to improve the timeliness and accuracy of RTHMs, which includes the following five steps: flow data processing, establishing nodal water demand (NWD) prediction models, node grouping, data assimilation (DA) and uncertainty analysis. Based on the actual network data, the performance of two data processing methods and three machine learning algorithms are, respectively, compared, and the best is selected for modeling. In the establishment of the hourly NWD prediction models, massive data, including flow measurement and data of all 26 input variables on climate, time and social influencing factors are used. It is found that the time features are the most important model input parameter. Application results of actual network prove that the flow data processing method, accurate NWD prediction, node grouping and Kalman filter-based DA method reduce the uncertainty in the RTHM and improve its timeliness and accuracy, so as to obtain the real-time state estimation of the WDS. Accurate NWD estimation (especially in the high-demand period) and combining RTHM with DA have a great influence on the uncertainty reduction in water pressure estimation, although uncertainty is weakened in the propagation process.

实时水力模型（RTHM）是配水系统（WDS）管理中的关键辅助工具，其性能直接影响辅助决策。本研究开发了一个提高RTHMs时效性和准确性的框架，包括以下五个步骤：流量数据处理、建立节点需水量（NWD）预测模型、节点分组、数据同化（DA）和不确定性分析。基于实际网络数据，分别对比两种数据处理方法和三种机器学习算法的性能，选择最佳进行建模。在建立每小时 NWD 预测模型时，使用了海量数据，包括流量测量和所有 26 个输入变量的气候、时间和社会影响因素的数据。发现时间特征是最重要的模型输入参数。实际网络应用结果证明，流数据处理方法、准确的NWD预测、节点分组和基于卡尔曼滤波器的DA方法降低了RTHM的不确定性，提高了实时性和准确性，从而获得了实时状态估计WDS。准确的 NWD 估计（特别是在高需求时期）以及 RTHM 与 DA 的结合对水压估计的不确定性降低有很大影响，尽管在传播过程中不确定性被削弱。节点分组和基于卡尔曼滤波器的DA方法减少了RTHM中的不确定性，提高了实时性和准确性，从而获得了WDS的实时状态估计。准确的 NWD 估计（特别是在高需求时期）以及 RTHM 与 DA 的结合对水压估计的不确定性降低有很大影响，尽管在传播过程中不确定性被削弱。节点分组和基于卡尔曼滤波器的DA方法减少了RTHM中的不确定性，提高了实时性和准确性，从而获得了WDS的实时状态估计。准确的 NWD 估计（特别是在高需求时期）以及 RTHM 与 DA 的结合对水压估计的不确定性降低有很大影响，尽管在传播过程中不确定性被削弱。