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

arXiv: https://arxiv.org/abs/2503.17167 · HTML: https://mlsanity.com/paper/2503.17167/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Physics-Informed Graph Neural Networks for Water Distribution Systems](https://mlsanity.com/paper/2403.18570/) (2024)
- [Graph Neural Networks for Pressure Estimation in Water Distribution Systems](https://mlsanity.com/paper/2311.10579/) (2023)
- [Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks](https://mlsanity.com/paper/2404.15386/) (2024)

## Built on by (most cited first, 3 of 3)

- [Conformal Prediction-Driven Adaptive Sampling for Digital Water Twins](https://mlsanity.com/paper/2511.05610/) (2025)
- [A Unified Framework for Structured Flow Modeling: From Representation to Verification and Model Discovery](https://mlsanity.com/paper/2605.18250/) (2026)
- [T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation](https://mlsanity.com/paper/2606.30011/) (2026)

## Abstract (arXiv)

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.