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 co...
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| author | Andrés Tello Huy Truong Alexander Lazovik Victoria Degeler |
| author_facet | Andrés Tello Huy Truong Alexander Lazovik Victoria Degeler |
| author_sort | Andrés Tello |
| collection | DOAJ |
| description | 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-sized publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky10. In total, 1,394,400 h of WDN data operating under normal conditions are made available to the community. |
| format | Article |
| id | doaj-art-5dbf3452e8954c99b0e0c1cf01a9f285 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-5dbf3452e8954c99b0e0c1cf01a9f2852025-08-20T03:43:15ZengMDPI AGEngineering Proceedings2673-45912024-09-016915010.3390/engproc2024069050Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution NetworksAndrés Tello0Huy Truong1Alexander Lazovik2Victoria Degeler3Bernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsBernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsBernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsInformatics Institute, University of Amsterdam, 1098 XH Amsterdam, The NetherlandsCurrently, 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-sized publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky10. In total, 1,394,400 h of WDN data operating under normal conditions are made available to the community.https://www.mdpi.com/2673-4591/69/1/50large-scale datasetswater distribution systemswater distribution networksstate estimationpressure estimationdemand forecasting |
| spellingShingle | Andrés Tello Huy Truong Alexander Lazovik Victoria Degeler Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks Engineering Proceedings large-scale datasets water distribution systems water distribution networks state estimation pressure estimation demand forecasting |
| title | Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks |
| title_full | Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks |
| title_fullStr | Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks |
| title_full_unstemmed | Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks |
| title_short | Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks |
| title_sort | large scale multipurpose benchmark datasets for assessing data driven deep learning approaches for water distribution networks |
| topic | large-scale datasets water distribution systems water distribution networks state estimation pressure estimation demand forecasting |
| url | https://www.mdpi.com/2673-4591/69/1/50 |
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