A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks

• URL: http://arxiv.org/abs/2510.24228v1
• Date: Tue, 28 Oct 2025 09:39:41 GMT
• Title: A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks
• Authors: Luis Romero-Ben, Paul Irofti, Florin Stoican, Vicenç Puig,
• Abstract summary: This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF)<n>One approach uses a joint state vector with a single estimator, while the other uses a dual-estimator scheme.<n>We show several estimation results for the L-TOWN benchmark, allowing to discuss their properties in a real implementation.
• Score: 3.7373312968044643
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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