Sensor Placement Optimization in Sewer Networks: Machine Learning–Based Source Identification Approach

Abstract

Wastewater surveillance has recently emerged as a valuable tool for environmental and public health monitoring. By analyzing the constituents and biomarkers present in wastewater, stakeholders can gather critical information regarding contamination events and disease outbreaks. However, little attention has been given to the crucial question of where to collect water quality samples or place water quality sensors to maximize the usefulness of wastewater surveillance data. To address this gap, this study introduces a novel framework for sensor placement (SP) optimization in sewer networks. The objective of the optimization is to maximize both the observability and reliability of source identification (SI) under different scenarios. To achieve this objective, a machine learning–based SI model was integrated within the SP optimization framework. The SI model features a multilayer perceptron neural network model that was trained to forecast concentrations at various sensor locations, which were then propagated into a genetic algorithm that finds the optimal sensor network design that maximizes SI performance. The capabilities of the SP framework were demonstrated in a case study featuring a real-life, midsize sewer network. The SP framework was applied to multiple scenarios, including optimal design of a sensor network comprising one or more sensors, as well as optimal extension of existing sensor networks. The results showed that a clear trade-off exists between the sensor network’s observability and reliability, highlighting the importance of considering both metrics for SP optimization. Overall, this study offers a practical approach for SP optimization to improve environmental and public health monitoring in a variety of contexts.