Optimizing Sensor Placement in Urban Storm Sewers: A Data-Driven Sparse Sensing Approach

TL;DR

This paper introduces a data-driven sparse sensing framework that optimizes sensor placement in urban stormwater systems, enabling accurate flow reconstruction with minimal sensors, thus aiding flood prediction under resource constraints.

Contribution

The study develops a novel DSS framework combining SVD and QR factorization for sensor placement, validated on a real urban stormwater network, improving monitoring efficiency.

Findings

Achieved high reconstruction accuracy with 3 sensors (NSE 0.92-0.95)

Demonstrated robustness to measurement uncertainty and sensor failure

Balanced computational efficiency with physical interpretability

Abstract

Urban surface water flooding, triggered by intense rainfall overwhelming drainage systems, is increasingly frequent and widespread. While flood prediction and monitoring in high spatial-temporal resolution are desired, practical constraints in time, budget, and technology hinder its full implementation. How to monitor urban drainage networks and predict flow conditions under constrained resource is a major challenge. This study presents a data-driven sparse sensing (DSS) framework, integrated with EPA-SWMM, to optimize sensor placement and reconstruct peak flowrates in a stormwater system, using the Woodland Avenue catchment in Duluth, Minnesota, as a case study. We utilized a SWMM model to generate a training dataset of peak flowrate profiles across the stormwater network. Furthermore, we applied DSS - leveraging singular value decomposition for dimensionality reduction and QR factorization for sensor allocation - to identify the optimal monitoring nodes based on the simulated training dataset. We then validated the representativeness of these identified monitoring nodes by comparing the DSS-reconstructed peak flowrate profiles with those obtained from SWMM. Three optimally placed sensors among 77 nodes achieved satisfactory reconstruction performance with Nash-Sutcliffe Efficiency (NSE) values of 0.92-0.95 (25th to 75th percentiles). In addition, the model showed good robustness to uncertainty in measurements. Its robustness to sensor failures is location-dependent and improves with the number of sensors deployed. The framework balances computational efficiency and physical interpretability, enabling high-accuracy flow reconstruction with minimal sensors. This DSS framework can be further integrated with predictive models to realize flood early warning and real-time control under limited sensing and monitoring resource.