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, while repeated stratified cross-validation confirms this model ranking and permutation-importance and SHAP analyses identify pipe age and length as the dominant risk drivers. A budgeted maximum weighted coverage 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 optimizers converge on the same solution. Extending to K=400 exposes a 56.58% coverage ceiling—set jointly by residual network fragmentation and the upstream detection range, and specific to the baseline candidate set and radius—and a six-radius sensitivity study (200–2500 m) identifies detection range as the most influential design parameter over the ranges tested. Risk coverage can be nearly tripled by redeploying the existing 48 stations without purchasing additional sensors.