DiTEC-WDN: A Large-Scale Dataset of Water Distribution Network Scenarios under Diverse Hydraulic Conditions

2025-03-21

AI Summary

Privacy constraints hinder the sharing of real-world water distribution network (WDN) models, impeding the development of data-driven methods. To address this, we propose DiTEC-WDN—the first large-scale, publicly licensed synthetic WDN simulation dataset. It encompasses 36,000 operational scenarios spanning 24-hour and annual hydraulic conditions, yielding 228 million graph-structured hydraulic states. Our methodology integrates automated EPANET-based simulation, multi-objective parameter optimization, graph-state encoding, and rule-based consistency verification—ensuring hydraulic fidelity while eliminating privacy risks entirely. DiTEC-WDN supports multi-granularity tasks, including graph-level, node-level, edge-level regression, and time-series forecasting. It has already enabled training and benchmarking of multiple AI models for water systems, filling a critical gap in publicly available benchmarks and advancing standardization in AI research for the water industry.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Privacy restrictions limit sharing real-world water network data
• Lack of large-scale datasets for water network machine learning
• Need synthetic but realistic hydraulic scenarios for research

Innovation

• Automated pipeline optimizes hydraulic parameters
• Generates synthetic hydraulically realistic states
• Large-scale graph-based states for ML tasks