评估用于水分网络的声泄漏检测模型的可通用性和可转移性
Chang Wang1, Zhigang Liu2, Jie Fei2
1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China.
Water research
|July 27, 2025
概括
这项研究通过提高模型通用性来增强水分发行网络 (WDNs) 的声学泄漏检测 (ALD). 微调模型显著提高了准确性,并减少了跨领域应用的数据需求.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 声波泄漏检测 (ALD) 对于水分网络 (WDN) 的安全至关重要.
- 目前的ALD模型因环境变化和数据稀缺而难以跨域部署.
研究的目的:
- 系统地研究ALD模型在多种WDN中的通用性和可转移性.
- 评估不同的跨领域转移策略,以提高ALD模型的性能.
主要方法:
- 在多源WDN数据上对ALD模型进行全面的跨领域评估.
- 全球模型的比较,直接转移,特征提取和微调策略.
- 转移不对称性和目标域数据要求的分析.
主要成果:
- 在多区域WDN上训练的全球模型显示了改进的泛化 (约. 2%的准确度增加).
- 微调实现了高传输精度 (96.8%的交叉材料,96.2%的交叉直径),优于其他方法.
- 转移不对称与数据分布有关;金属到非金属和大到小直径的转移显示出更好的适应性.
- 微调减少了50%的目标域数据需求,同时保持了准确性.
结论:
- 微调是跨域ALD模型转移的优越策略.
- 了解转移不对称性有助于优化ALD模型部署.
- 减少的数据需求使得人工智能驱动的ALD对各种WDN更加实用.
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