# Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks

arXiv: https://arxiv.org/abs/2404.15386 · HTML: https://mlsanity.com/paper/2404.15386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Graph Neural Networks for Pressure Estimation in Water Distribution Systems](https://mlsanity.com/paper/2311.10579/) (2023)
- [Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems](https://mlsanity.com/paper/2010.06460/) (2020)

## Built on by (most cited first, 2 of 2)

- [DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks](https://mlsanity.com/paper/2503.17167/) (2025)
- [Graph Attention Networks with Physical Constraints for Anomaly Detection](https://mlsanity.com/paper/2601.12426/) (2026)

## Abstract (arXiv)

Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. In this work, we provide a collection of datasets that includes several small and medium size publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky13. In total 1,394,400 hours of WDNs data operating under normal conditions is made available to the community.