Yu Zhiji, Huang Biao, Zhu David Z
School of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China.
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2.
Water Sci Technol. 2025 Jun;91(12):1307-1329. doi: 10.2166/wst.2025.079. Epub 2025 Jun 20.
Online monitoring is increasingly essential for the effective management and operation of urban sewer systems, yet resource limitations necessitate careful planning of sensor deployment. This study aims to address the impact of time lags on monitoring point selection in urban drainage systems using unsupervised machine learning techniques. A novel method is introduced to determine the optimal number and placement of sensors in manholes, using cluster analysis informed by simulated time-series data. The proposed methodology involves two sequential stages: the first stage clusters time-series data based on morphology similarity using the time-lagged cross-correlation (TLCC) coefficient, which measures the temporal alignment between datasets. The second stage further refines these clusters by considering magnitude similarity, employing dynamic time warping distance to quantify shape-based similarities and improve clustering accuracy. The proposed approach allows for flexible threshold adjustments to accommodate specific engineering requirements, enabling the design of monitoring strategies tailored to a predetermined number of locations. Furthermore, the study explores the impact of rainfall intensity on sensor placement, providing actionable guidance for sewer managers to improve monitoring efficiency and address urban water management challenges.
在线监测对于城市排水系统的有效管理和运行日益重要，但资源限制使得传感器部署需要精心规划。本研究旨在利用无监督机器学习技术解决时间滞后对城市排水系统监测点选择的影响。引入了一种新颖的方法，利用模拟时间序列数据提供的聚类分析来确定沙井中传感器的最佳数量和位置。所提出的方法包括两个连续阶段：第一阶段使用时间滞后互相关（TLCC）系数基于形态相似性对时间序列数据进行聚类，该系数测量数据集之间的时间对齐。第二阶段通过考虑幅度相似性进一步细化这些聚类，采用动态时间规整距离来量化基于形状的相似性并提高聚类精度。所提出的方法允许灵活调整阈值以适应特定的工程要求，从而能够设计针对预定数量位置的监测策略。此外，该研究探讨了降雨强度对传感器放置的影响，为下水道管理人员提高监测效率和应对城市水管理挑战提供了可操作的指导。
