| | --- |
| | 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 |
| |
|
| |  |
| |
|
| | 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). |
| |
|
| | 2. The custom **water factors** are in [water_factors.py](water_factors.py). |
| |
|
| | 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). |
| |
|
| |  |
| |
|
| | 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). |
