Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain
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 pr...
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MDPI AG
2024-09-01
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| author | Mariacrocetta Sambito Antonietta Simone |
| author_facet | Mariacrocetta Sambito Antonietta Simone |
| author_sort | Mariacrocetta Sambito |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea04afc2d708486eb5e835dacb71cb88 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-ea04afc2d708486eb5e835dacb71cb882025-08-20T03:43:15ZengMDPI AGEngineering Proceedings2673-45912024-09-016915810.3390/engproc2024069058Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search DomainMariacrocetta Sambito0Antonietta Simone1Department of Engineering and Architecture, University of Enna “Kore”, 94100 Enna, ItalyDepartment of Engineering and Geology, University “G. D’Annunzio” of Chieti Pescara, 65127 Pescara, ItalyThe 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.https://www.mdpi.com/2673-4591/69/1/58Bayesian decision networkUDN monitoringcontaminantcomplex network theoryreduced search domain |
| spellingShingle | Mariacrocetta Sambito Antonietta Simone Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain Engineering Proceedings Bayesian decision network UDN monitoring contaminant complex network theory reduced search domain |
| title | Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain |
| title_full | Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain |
| title_fullStr | Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain |
| title_full_unstemmed | Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain |
| title_short | Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain |
| title_sort | improving the performance of bayesian decision networks for water quality sensor deployment in udns through a reduced search domain |
| topic | Bayesian decision network UDN monitoring contaminant complex network theory reduced search domain |
| url | https://www.mdpi.com/2673-4591/69/1/58 |
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