Summary of Large-scale Multipurpose Benchmark Datasets For Assessing Data-driven Deep Learning Approaches For Water Distribution Networks, by Andres Tello et al.
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
by Andres Tello, Huy Truong, Alexander Lazovik, Victoria Degeler
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A newly developed collection of datasets for assessing data-driven deep learning approaches in Water Distribution Networks (WDNs) has been released. The dataset includes various small and medium-sized publicly available WDNs, providing a total of 1,394,400 hours of operational data under normal conditions. This is a crucial step forward in facilitating the evaluation and training of models for WDN management, as most current studies rely on researchers generating their own data through computationally intensive simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out how water flows through pipes. Right now, it’s hard for scientists to test new ideas because they have to create their own fake data from scratch! A team of experts is changing this by sharing real-life data about 11 different pipe systems. This means other researchers can use this data to train and test their models, making it easier to develop better ways to manage our water supplies. |
Keywords
» Artificial intelligence » Deep learning
