Dataset Open Access

Dataset of BattLeDIM: Battle of the Leakage Detection and Isolation Methods

Stelios G. Vrachimis; Demetrios G. Eliades; Riccardo Taormina; Avi Ostfeld; Zoran Kapelan; Shuming Liu; Marios S. Kyriakou; Pavlos Pavlou; Mengning Qiu; Marios Polycarpou

Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may lead to water losses. Typically, these water losses are due to background leakages and pipe bursts which may occur anywhere within the distribution network. Background leakages are normally difficult to detect due to their small size, whereas pipe bursts are easier to locate as they are of larger size and may appear on the surface. The early detection and localization of some leakage event is extremely important, as this would reduce the time required for accommodating the event and therefore reducing the risk of further infrastructure degradation, contamination events and consumer complaints.

In previous years, a number of methodologies have been proposed to detect and isolate the location of leakage events using various types of sensor measurements. These methods were commonly evaluated on private commercial datasets, and as a result, it is not possible to objectively compare these methods in their ability to detect and isolate leaks. In the past year, a leakage detection dataset has been proposed, LeakDB, based on benchmark networks and created using the WNTR tool, using pressure-driven demands and realistic leakage modelling. Inspired by the “BATtle of the Attack Detection ALgorithms” (BATADAL), which focused on the detection of cyber-physical attacks, our team decided to organize a similar “battle” focusing on leakage events.

The Battle of the Leakage Detection and Isolation Methods (BattLeDIM),  aims at objectively comparing the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within water distribution networks. Participants may use different types of tools and methods, including (but not limited to) engineering judgement, machine learning, statistical methods, signal processing, and model-based fault diagnosis approaches.

Files (550.5 MB)
Name Size
2018_Fixed_Leakages_Report.txt
md5:50b1f8b7ba0db5a19cedbb060c63d912
317 Bytes Download
2018_Leakages.csv
md5:c2c5fab90420da44f02e29775050abfe
6.2 MB Download
2018_SCADA.xlsx
md5:20c1224db8e379e31eba301d852e5cf7
92.2 MB Download
2018_SCADA_Demands.csv
md5:8803afcb1da2428de169237be3d911fb
56.5 MB Download
2018_SCADA_Flows.csv
md5:d0602c06946b46287e956f007e4264ee
4.0 MB Download
2018_SCADA_Levels.csv
md5:92a512d6749ddf66fd6d99f2bdde6cc4
2.7 MB Download
2018_SCADA_Pressures.csv
md5:d389d8541350c19ff0bfc6b80f246d35
22.6 MB Download
2019_Leakages.csv
md5:e1f0a43683813a90a9fec8562dde599b
10.4 MB Download
2019_SCADA.xlsx
md5:80242d5f59d39d0ef2306e2351c28367
92.3 MB Download
2019_SCADA_Demands.csv
md5:b3e111de397a1b06d33b8a9f13bc997d
56.5 MB Download
2019_SCADA_Flows.csv
md5:28fc99fdcbf80fcd26079e7fe602d6dc
4.1 MB Download
2019_SCADA_Levels.csv
md5:e5a8050bd38729e4b7648b9d52abd5b1
2.7 MB Download
2019_SCADA_Pressures.csv
md5:5ea1e46d3f2f0a89a3f98d6fd39a851d
22.6 MB Download
dataset_configuration.yaml
md5:48486401f5b4d0447023f5ce5d242c52
3.9 kB Download
L-TOWN.inp
md5:bfbb16b8b463e0576200ba6ad360f68e
412.2 kB Download
L-TOWN_Real.inp
md5:40fb38de25ed692a5ddd30e0f5fae332
177.2 MB Download
README.txt
md5:7ad5cdc2eebfe78197a3412e80d07ddc
5.3 kB Download
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