DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks

Huy Truong, Andrés Tello, Alexander Lazovik, Victoria Degeler
3/21/2025
cs.LGcs.AI

No official code found. Best implementation: DiTEC-project/DiTEC_WDN_dataset (13 stars, MIT, last commit 2026-04-23).

Abstract

Privacy restrictions hinder the sharing of real-world Water Distribution Network (WDN) models, limiting the application of emerging data-driven machine learning, which typically requires extensive observations. To address this challenge, we propose the dataset DiTEC-WDN that comprises 36,000 unique scenarios simulated over either short-term (24 hours) or long-term (1 year) periods. We constructed this dataset using an automated pipeline that optimizes crucial parameters (e.g., pressure, flow rate, and demand patterns), facilitates large-scale simulations, and records discrete, synthetic but hydraulically realistic states under standard conditions via rule validation and post-hoc analysis. With a total of 228 million generated graph-based states, DiTEC-WDN can support a variety of machine-learning tasks, including graph-level, node-level, and link-level regression, as well as time-series forecasting. This contribution, released under a public license, encourages open scientific research in the critical water sector, eliminates the risk of exposing sensitive data, and fulfills the need for a large-scale water distribution network benchmark for study comparisons and scenario analysis.

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Code Implementations(1)

DiTEC-project/DiTEC_WDN_datasetCommunity100%
MIT

This repository contains parameter generation, simulation, and encapsulation code for the DiTEC-WDN dataset. Feel free to use it on your "private" WDN!

131PythonDec 12, 20246 months agoMIT
datasetdataset-generationdatasetsgraph-datasetgraph-datasets+5 more

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Cite this paper

@article{truong2025ditecwdn,
  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},
  url    = {http://arxiv.org/abs/2503.17167v2}
}

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