Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
This addresses a data availability problem for researchers and practitioners in water infrastructure management, though it is incremental as it compiles existing data rather than introducing new methods.
The authors tackled the lack of ready-to-use benchmark datasets for data-driven deep learning in water distribution networks by providing a collection of datasets, including 1,394,400 hours of operational data from multiple 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.