The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF), fusing pressure, demand and flow data for head and flow estimation. One approach uses a joint state vector with a single estimator, while the other uses a dual-estimator scheme. We analyse their main characteristics, discussing differences, advantages and limitations, and compare them theoretically in terms of accuracy and complexity. Finally, we show several estimation results for the L-TOWN benchmark, allowing to discuss their properties in a real implementation.

翻译：现代城市的可持续性高度依赖于高效的水分配管理，包括有效的压力控制以及泄漏检测与定位。因此，获取关于管网水力状态的准确信息至关重要。本文比较了两种基于无迹卡尔曼滤波器（UKF）的数据驱动状态估计方法，该方法融合压力、需水量和流量数据，用于水头和流量估计。一种方法采用单一估计器处理联合状态向量，而另一种则采用双重估计器方案。我们分析了它们的主要特性，讨论了差异、优势和局限性，并从精度和复杂度两方面进行了理论比较。最后，我们展示了L-TOWN基准测试的若干估计结果，从而能够在实际实现中讨论其特性。