Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<|reference_start|>Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks: 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.<|reference_end|>
@article{tello2024large-scale,
    title={Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven
  Deep Learning Approaches For Water Distribution Networks},
    author={Andres Tello, Huy Truong, Alexander Lazovik and Victoria Degeler},
    journal={arXiv preprint arXiv:2404.15386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15386},
    primaryClass={cs.LG cs.AI}
}