Development and Application of a River–Sewer Water Level Correlation Model for Identifying Inflow and Infiltration Diagnosis: A Case Study of Zhongshan’s Regional Sewage Network

Identifying inflow and infiltration (I/I) in urban sewer networks is challenging due to nonlinear hydraulic interactions and time-lag effects between river stages and pipeline water levels. This study proposes a multi-scale fusion correlation model integrating Dynamic Time Warping (DTW), Pearson correlation, and Spearman rank correlation coefficients. The framework evaluates water level sequences across temporal windows (2, 6, and 12 h) under flexible displacement constraints (1 and 2 h), utilizing a dynamic weight-allocation mechanism based on sequence volatility and data density. Leveraging a 12-month monitoring dataset from 513 sensing devices in Zhongshan City, China, the model was evaluated on 36 typical water-level sequences. It achieved a classification accuracy of 91.7% for low-correlation sequences and perfect accuracy (100%) for both medium- and high-correlation levels. Furthermore, practical deployment in the Shaxi–Qijiang Highway section and Yicheng Area successfully isolated multiple vulnerable pipe segments suffering from Baishiyong River (tidal) water intrusion, yielding an empirical field-verification hit rate of 83.3%. The results demonstrate that the proposed framework effectively overcomes temporal asynchrony and nonlinear hydraulic noise, providing a robust, data-driven diagnostic tool for urban drainage infrastructures.