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

Andres Tello, Huy Truong, Alexander Lazovik, Victoria Degeler
4/23/2024
cs.LGcs.AI

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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.

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Cite this paper

@article{tello2024largescale,
  title  = {Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks},
  author = {Andres Tello and Huy Truong and Alexander Lazovik and Victoria Degeler},
  year   = {2024},
  eprint = {2404.15386},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.15386v1}
}

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