Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach
Contamination of treated drinking water is a critical public health and safety concern. In this study, a multi-objective Bayesian optimization (MOBO) framework is proposed to optimize operational response to contamination in drinking water distribution systems (WDSs). The optimization framework aims...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Water |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frwa.2025.1547112/full |
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| author | Khalid Alnajim Ahmed A. Abokifa |
| author_facet | Khalid Alnajim Ahmed A. Abokifa |
| author_sort | Khalid Alnajim |
| collection | DOAJ |
| description | Contamination of treated drinking water is a critical public health and safety concern. In this study, a multi-objective Bayesian optimization (MOBO) framework is proposed to optimize operational response to contamination in drinking water distribution systems (WDSs). The optimization framework aims to balance the conflicting objectives of minimizing response time while maximizing water quality metrics after contamination events. This was achieved by simultaneously optimizing two objective functions: the number of field operations (i.e., valve-closings and hydrant-openings), and the total contaminant mass consumed. The framework integrates a WDS simulation model, EPANET, within the proposed framework to simulate the implementation of response actions to various contamination events. Simulation results are then propagated into MOBO to generate Pareto-optimal solutions of the objective functions. A sensitivity analysis was conducted to tune the hyperparameters of the MOBO algorithm, including the covariance kernel of the surrogate model. Two case study WDSs with varying sizes and topological complexities were used to evaluate the performance of the proposed MOBO framework. Additionally, the performance of the MOBO algorithm was compared to the commonly used NSGA-II algorithm. The results showed that the proposed MOBO framework can identify optimal response actions to rapidly and efficiently improve water quality in the wake of contamination events in WDSs. |
| format | Article |
| id | doaj-art-320ab320abfa46ceb3d8a3f24c852147 |
| institution | Kabale University |
| issn | 2624-9375 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Water |
| spelling | doaj-art-320ab320abfa46ceb3d8a3f24c8521472025-08-20T03:53:56ZengFrontiers Media S.A.Frontiers in Water2624-93752025-05-01710.3389/frwa.2025.15471121547112Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approachKhalid Alnajim0Ahmed A. Abokifa1Department of Civil Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Civil, Materials, and Environmental Engineering, The University of Illinois Chicago, Chicago, IL, United StatesContamination of treated drinking water is a critical public health and safety concern. In this study, a multi-objective Bayesian optimization (MOBO) framework is proposed to optimize operational response to contamination in drinking water distribution systems (WDSs). The optimization framework aims to balance the conflicting objectives of minimizing response time while maximizing water quality metrics after contamination events. This was achieved by simultaneously optimizing two objective functions: the number of field operations (i.e., valve-closings and hydrant-openings), and the total contaminant mass consumed. The framework integrates a WDS simulation model, EPANET, within the proposed framework to simulate the implementation of response actions to various contamination events. Simulation results are then propagated into MOBO to generate Pareto-optimal solutions of the objective functions. A sensitivity analysis was conducted to tune the hyperparameters of the MOBO algorithm, including the covariance kernel of the surrogate model. Two case study WDSs with varying sizes and topological complexities were used to evaluate the performance of the proposed MOBO framework. Additionally, the performance of the MOBO algorithm was compared to the commonly used NSGA-II algorithm. The results showed that the proposed MOBO framework can identify optimal response actions to rapidly and efficiently improve water quality in the wake of contamination events in WDSs.https://www.frontiersin.org/articles/10.3389/frwa.2025.1547112/fullBayesian optimizationcontamination responsemulti-objective optimizationwater distributiondrinking water |
| spellingShingle | Khalid Alnajim Ahmed A. Abokifa Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach Frontiers in Water Bayesian optimization contamination response multi-objective optimization water distribution drinking water |
| title | Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach |
| title_full | Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach |
| title_fullStr | Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach |
| title_full_unstemmed | Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach |
| title_short | Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach |
| title_sort | operational response to contamination in water distribution systems a multi objective bayesian optimization approach |
| topic | Bayesian optimization contamination response multi-objective optimization water distribution drinking water |
| url | https://www.frontiersin.org/articles/10.3389/frwa.2025.1547112/full |
| work_keys_str_mv | AT khalidalnajim operationalresponsetocontaminationinwaterdistributionsystemsamultiobjectivebayesianoptimizationapproach AT ahmedaabokifa operationalresponsetocontaminationinwaterdistributionsystemsamultiobjectivebayesianoptimizationapproach |