综述机器学习在供水管网中的应用挑战与方法
Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
- 梳理供水管网中机器学习的核心任务与技术路径
- 提出泄漏检测与定位的评估基准和数据集框架
- 适合水务智能运维与工业AI研究者参考
随着气候变化导致饮用水资源减少，供水管网的规划与控制研究日益重要。当前多数方法依赖水力学与工程经验，但随着传感器普及，机器学习成为有前景的工具。本文梳理了供水管网中的主要任务，分析了领域特性对机器学习带来的挑战与机遇，并提供了技术工具包：包括评估基准与泄漏检测与定位的系统性综述。研究覆盖典型应用场景，为后续算法开发与验证提供结构化参考。
原文摘要 · Abstract (English)
Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.
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