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

Andrés Tello*, Huy Truong*, Alexander Lazovik, Victoria Degeler

*Corresponding author voor dit werk

OnderzoeksoutputAcademic

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Samenvatting

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, Ky10, and Ky13. In total 1,394,400 hours of WDNs data operating under normal conditions is made available to the community.
Originele taal-2English
TitelEngineering Proceedings
UitgeverijMultidisciplinary Digital Publishing Institute (MDPI)
Aantal pagina's6
StatusAccepted/In press - 6-mei-2024
Evenement3rd International Joint Conference on Water Distribution Systems Analysis &
Computing and Control for the Water Industry
- University of Ferrara, Science & Technology Campus, Ferrara, Italy
Duur: 1-jul.-20244-jul.-2024
https://wdsa-ccwi2024.it

Conference

Conference3rd International Joint Conference on Water Distribution Systems Analysis &
Computing and Control for the Water Industry
Verkorte titelWDSA/CCWI
Land/RegioItaly
StadFerrara
Periode01/07/202404/07/2024
Internet adres

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