# Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks

arXiv: https://arxiv.org/abs/2311.15875 · HTML: https://mlsanity.com/paper/2311.15875/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization](https://mlsanity.com/paper/2304.10932/) (2023)

## Built on by (most cited first, 3 of 3)

- [Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization](https://mlsanity.com/paper/2412.11687/) (2024)
- [Factor Graph Optimization for Leak Localization in Water Distribution Networks](https://mlsanity.com/paper/2509.10982/) (2025)
- [A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks](https://mlsanity.com/paper/2510.24228/) (2025)

## Abstract (arXiv)

In this paper, we present a nodal hydraulic head estimation methodology for water distribution networks (WDN) based on an Unscented Kalman Filter (UKF) scheme with application to leak localization. The UKF refines an initial estimation of the hydraulic state by considering the prediction model, as well as available pressure and demand measurements. To this end, it provides customized prediction and data assimilation steps. Additionally, the method is enhanced by dynamically updating the prediction function weight matrices. Performance testing on the Modena benchmark under realistic conditions demonstrates the method's effectiveness in enhancing state estimation and data-driven leak localization.