Risk-Driven Sensor Placement in Sewer Networks: A Descriptive–Predictive–Prescriptive Framework

Sanitary sewer collection systems are among the least observable urban infrastructure assets, with most utilities operating fewer than one sensor per several hundred pipes; placement drives operational value. We develop DPP-SP, a Descriptive–Predictive–Prescriptive Sensor-Placement framework that links machine-learning failure prediction with risk-weighted maximum-coverage placement and apply it to a 33,349-pipe sewer system. The geographic information system (GIS) topology is rebuilt, raising the largest connected component from 29.8% to 89.4% of nodes. Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and a multilayer perceptron (MLP) are trained on combined 2020–2025 failure data; RF achieves the highest receiver-operating-characteristic area under the curve (ROC-AUC) of 0.7626 and supplies per-pipe risk weights. A maximum weighted set-cover problem is solved over 680 candidate sites using greedy, genetic algorithm (GA) and tabu search (TS). At K=48, RF with greedy covers 32.26% of network risk against an 11.73% baseline, a 174.9% improvement; all three metaheuristics converge on the same solution. Extending to K=400 exposes a 56.58% structural ceiling due to isolated fragments, and a six-radius sensitivity study (200–2,500 m) identifies detection range as the dominant design parameter. Risk coverage can be nearly tripled by redeploying the existing 48 stations at no capital cost.