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

TL;DR

This paper introduces a comprehensive collection of large-scale, publicly available water distribution network datasets to facilitate the development and evaluation of data-driven deep learning models in water management.

Contribution

It provides a diverse set of 1,394,400 hours of operational data from multiple water distribution networks, addressing the scarcity of ready-to-use benchmark datasets.

Findings

Provides extensive datasets for WDNs

Enables standardized evaluation of deep learning models

Supports research in water network management

Abstract

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.