# Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization

arXiv: https://arxiv.org/abs/2304.10932 · HTML: https://mlsanity.com/paper/2304.10932/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Fault Handling in Large Water Networks with Online Dictionary Learning](https://mlsanity.com/paper/2003.08483/) (2020)
- [A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks](https://mlsanity.com/paper/1606.01754/) (2016)
- [Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation](https://mlsanity.com/paper/2110.06372/) (2021)

## Built on by (most cited first, 4 of 4)

- [Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks](https://mlsanity.com/paper/2311.15875/) (2023)
- [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)

This article presents a leak localization methodology based on state estimation and learning. The first is handled by an interpolation scheme, whereas dictionary learning is considered for the second stage. The novel proposed interpolation technique exploits the physics of the interconnections between hydraulic heads of neighboring nodes in water distribution networks. Additionally, residuals are directly interpolated instead of hydraulic head values. The results of applying the proposed method to a well-known case study (Modena) demonstrated the improvements of the new interpolation method with respect to a state-of-the-art approach, both in terms of interpolation error (considering state and residual estimation) and posterior localization.