Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain †

Abstract

The contamination of urban drainage systems (UDNs) represents a serious threat to the environment and public health. Treatment plants are often inefficient in their removal, making timely identification and isolation interventions necessary. In this regard, various monitoring strategies have been proposed, among which the Bayesian decision network (BDN) approach has proven to be very effective, although also very complex. To reduce their level of complexity, it is usual to optimize them using approaches based on preconditioning. The present work fits into this framework by proposing a two-phase strategy aimed at identifying an optimal monitoring system for UDNs. The first phase involves reducing the search domain of the system using a complex network theory (CNT) topological metric adapted to infrastructure systems; the second phase implements the Bayesian approach to the new search space to optimize the position of the sensors in the network. The results are promising and reveal that the strategy could be valuable to water utilities.