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Dataset Card for DiTEC-WDN

Dataset Summary

DiTEC-WDN consists of 36 Water Distribution Networks (WDNs). Each network has unique 1,000 scenarios with distinct characteristics. Scenario represents a timeseries of undirected shared-topology graphs, referred to as states or snapshots. In terms of graph-ml, it can be seen as a spatiotemporal graph where nodes and edges are multivariate time series.

To begin with, you should have a look at the list of 72 configs in this README.md. The naming format is <network_name>_<storage_size>_<duration>_<general_component>. Here, please ensure sufficient storage before processing the dataset.

Let refer to this example code:

from datasets import load_dataset
raw_dataset = load_dataset(path="rugds/ditec-wdn", name="epanet2_6GB_1Y_node")

This will download parquet files containing nodal features of network epanet2.

Supported Tasks and Leaderboards

• graph-ml: The dataset can be used to train a model for graph-related tasks, including node-level, link-level, and graph-level regressions.
• time-series-forecasting: The dataset can be used to train a model for time series forecasting tasks, including multivariate state estimation and next state forecasting.

Success on both tasks is typically measured by achieving:
(1) low Mean Absolute Error (MAE)
(2) low Mean Absolute Percentage Error (MAPE)
(3) low (Root) Mean Squared Error (RMSE/MSE)
(4) high coefficient of determination (R**2)
(5) high Nash–Sutcliffe Efficiency (NSE)

Languages

en

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

The dataset is designed to (1) encourage open scientific research in the fundamental field of water, (2) eliminate the risk of exposing sensitive data while addressing strict privacy concerns that hinder advancements in machine learning, and (3) fulfill the need for a large-scale water distribution network benchmark for study comparisons and scenario analysis.

Source Data

The source data consists of simulation metadata files (.INP) collected from public resources. However, we retain only topology and component names, discarding all hydraulic-related values (e.g., elevation, demand time series, diameter, etc). These discarded values are later re-synthesized by our generator. As a result, the dataset contains no real or sensitive information from the original metadata or real-world systems.

Initial Data Collection and Normalization

The list below briefly describes the original water distribution networks:

WDN	Junctions	Pipes	Reservoirs	Tanks	Pumps	Patterns
ky1 [Jolly2014]	856	985	1	2	1	2
ky2 [Jolly2014]	811	1125	1	3	1	3
ky3 [Jolly2014]	269	371	3	3	5	3
ky4 [Jolly2014]	959	1158	1	4	2	3
ky5 [Jolly2014]	420	505	4	3	9	3
ky6 [Jolly2014]	543	647	2	3	2	4
ky7 [Jolly2014]	481	604	1	3	1	4
ky8 [Jolly2014]	1325	1618	2	5	4	4
ky10 [Jolly2014]	920	1061	2	13	13	4
ky13 [Jolly2014]	778	944	2	5	4	3
ky14 [Jolly2014]	377	553	4	3	5	3
ky16 [Jolly2014]	791	915	3	4	7	3
ky18 [Jolly2014]	772	917	4	0	3	9
ky24_v [Jolly2014]	288	292	2	0	0	3
19 Pipe System [Wood1972]	12	21	2	0	0	3
Anytown [Walski1987]	19	41	3	0	1	1
new_york [Schaake1969]	19	42	1	0	0	4
Jilin [Bi2014]	27	34	1	0	0	1
hanoi [Fujiwara1990]	31	34	1	0	0	0
fossolo [Bragalli2008]	36	58	1	0	0	0
FOWM [Walski2005]	44	49	1	0	0	0
EPANET Net 3 [Clark1995]	92	119	2	3	2	5
FFCL-1 [Rossman1996]	111	126	0	1	0	3
Zhi Jiang [Zheng2011]	113	164	1	0	0	0
WA1 [Vasconcelos1997]	121	169	0	2	0	6
OBCL-1 [Vasconcelos1997]	262	289	1	0	1	5
modena [Bragalli2008]	268	317	4	0	0	0
NPCL-1 [Clark1994]	337	399	0	2	0	17
Marchi Rural [Marchi2014]	379	476	2	0	0	0
CTOWN [Ostfeld2012]	388	444	1	7	11	5
d-town [Marchi2014]	399	459	1	7	11	5
balerma [Reca2006]	443	454	4	0	0	0
L-TOWN [Vrachimis2020]	782	909	2	1	1	107
KL [Kang2012]	935	1274	1	0	0	0
Exnet [Farmani2004]	1891	2467	2	0	0	0
Large [Sitzenfrei2023]	3557	4021	1	0	0	0

Who are the source language producers?

The dataset was machine-generated. The process detail could be found in the co-existence paper (see Citation Information).

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

The known limitations have been discussed in the binding paper. Please see Citation Information.

Additional Information

Dataset Curators

This work is funded by the project DiTEC: Digital Twin for Evolutionary Changes in Water Networks (NWO 19454).

Licensing Information

CC BY-NC 4.0

Citation Information

For the dataset usage, please cite this:

@misc{truong2025dwd,
    title={DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks}, 
    author={Huy Truong and Andrés Tello and Alexander Lazovik and Victoria Degeler},
    year={2025},
    eprint={2503.17167},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2503.17167}, 
}

Contributions

We thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Hábrók high performance computing cluster.