Huy Cuong Truong

1. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks (A. Tello, H. Truong, A. Lazovik and V. Degeler), In Proceedings of the 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI), MDPI, 2024.

   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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   BibTeX

   
   @InProceedings{tello2024:wdsa, title = {Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks}, 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.}, author = {A. Tello and H. Truong and A. Lazovik and V. Degeler}, year = {2024}, language = {English}, booktitle = {Proceedings of the 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI)}, publisher = {MDPI}, url = {}, note = {In Press. A. Tello and H. Truong contributed equally to this work.} }

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2. Graph Neural Networks for Pressure Estimation in Water Distribution Systems (H. Truong, A. Tello, A. Lazovik and V. Degeler), In arXiv preprint arXiv:2311.10579, Arxiv, 2023.

   BibTeX

   
   @InProceedings{truong2023:arxiv, title = {Graph Neural Networks for Pressure Estimation in Water Distribution Systems}, author = {H. Truong and A. Tello and A. Lazovik and V. Degeler}, year = {2023}, language = {English}, booktitle = {arXiv preprint arXiv:2311.10579}, publisher = {Arxiv}, url = {}, note = {SUBMITTED FOR PUBLICATION IN WATER RESOURCES RESEARCH. H. Truong and A. Tello contributed equally to this work.} }

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