Optimizing the layout of urban drainage pipeline monitoring points using agglomerative hierarchical clustering and cross-correlation functions

Abstract

[Objective] With the increasing importance of urban drainage pipelines in flood management and water pollution control, there is an urgent need for optimized monitoring networks. [Methods] An efficient and accurate method for optimizing the layout of monitoring points in urban drainage pipelines to detect and identify illegal sewage discharge was proposec. A Storm Water Management Model(SWMM) was utilized to simulate pollutant transport within urban drainage pipelines and generate time-series data. An agglomerative hierarchical clustering algorithm was applied to classify the data and determine the optimal number of monitoring points in the drainage network. Node correlation was assessed through cross-correlation functions, selecting nodes with the highest coefficients to ensure comprehensive pollution conditions within the clusters. [Results] The proposed method was validated using a drainage network case from the SWMM manual, and it was determined that deploying three monitoring points is the optimal choice. The evaluation revealed a reliability of 91.86% and an average response time of 3.26 minutes at three monitoring points. Agglomerative hierarchical clustering outperformed the K-means algorithm in terms of monitoring point layout effectiveness, especially in covering the upstream, midstream, and downstream sections of the drainage network. [Conclusion] This method offers a new technical means for optimizing the layout of monitoring points in urban drainage pipelines, enhancing urban drainage monitoring and management and offering new perspectivesfor related research areas.

Keywords

urban drainage pipelines / monitoring point optimization / agglomerative hierarchical clustering algorithm / cross-correlation function / SWMM model