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Dataset results
11 results for “Water distribution network”
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
Pareto Fronts and metrics for the optimization design water distribution networks (Hanoi, Fossolo, Modena, and Balerma)
<p>Approximations to the Best-known PFs using NSGA-II retrofitted by OPUS for Hanoi, Fossolo, Modena, and Balerma, Approximations to the Best-known PFs using NSGA-II for Hanoi, Fossolo, Modena and Balerma, Hypervolume (HV) and Modified Inverted Generational Distance (IGD+) for Hanoi, Fossolo, Modena, and Balerma.</p> <ul> <li>Hanoi (30 simulations)</li> <li>Fossolo (30 simulations)</li> <li>Modena (1 simulation)</li> <li>Balerma (1 simulation)</li> </ul>
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
Dataset for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem
<p>This file consists all the data used in the manuscript " Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem "</p>
Pressure monitoring dataset and frequency domain analysis in Padua water distribution network
<p>The dataset includes:</p><ul><li>the acquired pressure signal at measurement section P036 with a high sampling frequency (indicated by "fre") for 24 hours;</li><li>the frequency domain analysis of three pressure datasets within a certain range, "omega", of frequencies</li></ul>
Minimizing Search Areas for Leak Detection in Water Distribution Networks - Code
<p>This database includes the code used to analyze and produce the results for the following research article:</p> <p>Minimizing Search Areas for Leak Detection in Water Distribution Networks by B. Snider, G. Lewis, A.S. Chen, L. Vamvakeridou-Lyroudia, S. Djordjevic, D.A. Savic. Journal of Hydroinformatics. (Accepted - awaiting publication).</p> <p> </p> <p> </p>
Data and code from: Deep reinforcement learning for pressure optimization in water distribution networks with multiple pumping stations: Case study
Open the record for dataset details and reuse information.
Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019
<p>Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019. The MNDWI was computed from Sentinel 2 images.</p>
Data set for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem
<p>This is the dataset for our manuscript titled "Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem".</p>
Water Distribution--Transportation Interface Network Data
<p>Water distribution network layouts (as .inp files) used in the water distribution-- transportation interface network study submitted for publication. </p>
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OpenNeuro
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