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DiTEC-WDN:跨多个配水网的液压场景的大规模数据集
Huy Truong1, Andrés Tello2, Alexander Lazovik3
1Bernoulli Institute, University of Groningen, Groningen, The Netherlands. h.c.truong@rug.nl.
Scientific data
|November 3, 2025
概括
一个新的数据集,DiTEC-WDN,为机器学习提供了36000个水分网 (WDN) 场景. 这些合成数据支持无隐私风险的WDN研究.
科学领域:
- 液压工程 液压工程 液压工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 由于隐私问题,现实世界水分配网络 (WDN) 模型很难共享.
- 这限制了数据驱动机器学习技术在水资源部门的应用.
- 机器学习模型通常需要广泛的观察.
研究的目的:
- 引入DiTEC-WDN,这是一个合成WDN场景的大规模数据集.
- 为了促进机器学习研究在WDN分析.
- 为WDN模型比较和场景分析提供一个基准.
主要方法:
- 开发了一个自动化管道,用于优化WDN参数和大规模模拟.
- 在短期 (24小时) 和长期 (1年) 期间生成了36000个独特的场景.
- 创建了2.28亿个基于图形的状态,使用规则验证和对液压现实性的后期分析.
主要成果:
- DiTEC-WDN数据集包含2.28亿个以图表为基础的状态.
- 该数据集支持各种机器学习任务,包括回归和时间序列预测.
- 场景涵盖了24小时和1年期间的36000个独特配置.
结论:
- DiTEC-WDN通过提供合成,液压现实的WDN数据来解决隐私问题.
- 该数据集的公开发布鼓励在水资源部门开展开放的科学研究.
- 这一基准数据集能够对WDNs进行可靠的研究比较和场景分析.
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