| --- |
| license: isc |
| tags: |
| - leak |
| - localization |
| - water-distribution-network |
| - fgo |
| - factor-graph-optimization |
| - estimation |
| - interpolation |
| - leak-localization |
| size_categories: |
| - 10M<n<100M |
| --- |
| # Factor Graph Optimization for Leak Localization in Water Distribution Networks |
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|  |
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| Implementation and experimental data for the [paper](https://arxiv.org/pdf/2509.10982) |
|
|
| > P. Irofti, L. Romero-Ben, F. Stoican, and V. Puig, |
| “Factor Graph Optimization for Leak Localization in Water |
| Distribution Networks," |
| pp. 1--12, 2025. |
|
|
| If you use our work in your research, please cite as: |
| ``` |
| @article{IRSP25_fgll, |
| author = {Irofti, P. and Romero-Ben, L. and Stoican, F. and Puig, V.}, |
| title = {Factor Graph Optimization for Leak Localization in Water |
| Distribution Networks}, |
| year = {2025}, |
| pages = {1-12}, |
| eprint = {2509.10982}, |
| archiveprefix = {arXiv}, |
| } |
| ``` |
|
|
| ## Prerequisite |
| Before running make sure you have installed the Python packages: |
| * [numpy](https://numpy.org/) |
| * [scipy](https://scipy.org/) |
| * [gtsam](https://gtsam.org/) |
| * [wntr](https://github.com/USEPA/WNTR) |
|
|
| ## Usage |
| Run [test_FGLL.py](test_FGLL.py) and set the network parameter to `Modena`, `LTOWN` or `toy_example`. Default is `Modena`. |
|
|
| ## Description |
| Detecting and localizing leaks in water distribution network systems is an important topic with direct environmental, economic, and social impact. |
| Our paper is the first to explore the use of factor graph optimization techniques for leak localization in water distribution networks, |
| enabling us to perform sensor fusion between pressure and demand sensor readings |
| and to estimate the network's temporal and structural state evolution across all network nodes. |
| The methodology introduces specific water network factors and proposes a new architecture composed of two factor graphs: |
| a leak-free state estimation factor graph and a leak localization factor graph. |
| When a new sensor reading is obtained, |
| unlike Kalman and other interpolation-based methods, |
| which estimate only the current network state, |
| factor graphs update both current and past states. |
| Results on Modena, L-TOWN and synthetic networks show that factor graphs are much faster than nonlinear Kalman-based alternatives such as the UKF, |
| while also providing improvements in localization compared to state-of-the-art estimation-localization approaches. |
|
|
| ## Contents |
| 1. The **Factor Graph Leak Localization** (FGLL) algorithm is in [FGLL.py](FGLL.py). |
|
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| 2. The custom **water factors** are in [water_factors.py](water_factors.py). |
|
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| 3. Specific water distribution network data are in [network_data](network_data). |
|
|
| ## Results |
|
|
| In the paper we compared our results with [GHR-S](https://www.sciencedirect.com/science/article/abs/pii/S0043135423001823?via%3Dihub), [GSI](https://github.com/luisromeroben/PhD/tree/master/Chapter3) and [UKF-AW-GSI](https://github.com/luisromeroben/D-UKF-AW-GSI). |
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|  |
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| Description: Normalized leak metric for each potential leak, comparing GHR-S, GSI, UKF-AW-GSI and FGLL. Each image encodes a colour code of the normalized metric of a node (x-axis) in a leak scenario (y-axis). |
