Andrés Tello
- Graph Neural Networks for Pressure Estimation in Water Distribution Systems ( ), In Water Resources Research, 2024.
Abstract
Abstract Pressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics-based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data-driven approaches have gained traction to overcome such limitations. In this work, we combine physics-based modeling and graph neural networks (GNN), a data-driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN-based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real-world scenarios. As a result, a new state-of-the-art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average.
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urldoi - Large-scale multipurpose benchmark datasets for assessing data-driven deep learning approaches for water distribution networks ( ), In Engineering Proceedings, MDPI, volume 69, 2024.
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- Graph neural networks for pressure estimation in water distribution systems ( ), In Water Resources Research, Wiley Online Library, volume 60, 2024.
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- DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks ( ), In , 2024.
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- Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks ( ), In 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI), 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-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.
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urldoi - Too Good To Be True: accuracy overestimation in (re) current practices for Human Activity Recognition ( ), In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2024.
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- Smartphone-based real-time indoor positioning using BLE beacons ( ), In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022.
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- Digital Twins: an enabler for digital transformation ( ), Chapter in The Digital Transformation handbook, 2021.
- Digital Twins: an enabler for digital transformation ( ), Chapter in The Digital Transformation handbook, 2021.
- Temporal Analysis of 911 Emergency Calls Through Time Series Modeling ( ), In The International Conference on Advances in Emerging Trends and Technologies, 2019.
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- Prediction of Imports of Household Appliances in Ecuador Using LSTM Networks ( ), In Conference on Information Technologies and Communication of Ecuador, 2019.
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- Modeling 911 emergency events in Cuenca-Ecuador using geo-spatial data ( ), In International Conference on Technology Trends, 2018.
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- LOD-GF: an integral linked open data generation framework ( ), In Conference on Information Technologies and Communication of Ecuador, 2018.
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- Semantic web and augmented reality for searching people, events and points of interest within of a university campus ( ), In 2017 XLIII Latin American Computer Conference (CLEI), 2017.
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- Tv program recommender using user authentication on middleware ginga ( ), In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), 2017.
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- Methodological guidelines for publishing library data as linked data ( ), In 2017 International Conference on Information Systems and Computer Science (INCISCOS), 2017.
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- Challenges and trends about smart big geospatial data: A position paper ( ), In 2017 IEEE International Conference on Big Data (Big Data), 2017.
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- Automatic RDF-ization of big data semi-structured datasets ( ), In Maskana, volume 7, 2016.
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- Detecting similar areas of knowledge using semantic and data mining technologies ( ), In Electronic Notes in Theoretical Computer Science, Elsevier, volume 329, 2016.
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- Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies ( ), In Maskana, volume 6, 2015.
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- An automatic method for the enrichment of dicom metadata using biomedical ontologies ( ), In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
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- Rdf-ization of dicom medical images towards linked health data cloud ( ), In VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, 2015.
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- WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies ( ), In 11th International Symposium on Medical Information Processing and Analysis, volume 9681, 2015.
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