通过域名转移在供水网络中进行跨区域泄漏检测
Ziyang Xu1, Rongsheng Liu2, Haixing Liu3
1School of Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China; Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
Water research
|December 16, 2025
概括
本研究引入了一个域调整框架,以改进机器学习泄漏检测系统. 新框架提高了模型在不同地区的普遍性,提高了智能基础设施管理的准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 有效的泄漏检测对于城市基础设施至关重要.
- 机器学习 (ML) 在泄漏检测方面提供了更高的准确性和效率.
- 机器学习模型的性能受到区域数据的限制,这阻碍了概括性,并需要广泛的本地数据集.
研究的目的:
- 为基于ML的泄漏检测提出一个域调整框架.
- 为了应对在不同地理区域有限的模型通用性的挑战.
- 在新的目标地区减少对高质量的标记数据集的需求.
主要方法:
- 开发了一个域调整框架,将知识从源域转移到目标域.
- 利用现有的标记数据集来适应新地区.
- 使用t分布式静态邻居嵌入 (t-SNE) 进行可视化和性能分析.
主要成果:
- 与基线模型相比,在跨区域传输 (香港到大连) 中获得了超过10%的更高准确性.
- 证明了框架在调整ML模型以适应新的区域数据特征方面的有效性.
- t-SNE可视化证实了改进的域适应性和泄漏检测性能.
结论:
- 拟议的领域适应框架显著提高了基于ML的泄漏检测系统的通用性和有效性.
- 这种方法降低了开发成本,并减少了在新地区需要广泛的数据标签的需求.
- 该研究通过提高ML模型性能来促进智能基础设施管理的更广泛采用.
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