Sensor Placement Optimization in Sewer Networks: Machine Learning–Based Source Identification Approach
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 11
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.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Funding by the National Science Foundation under Award No. 2134747 is gratefully acknowledged.
Author contributions: Aly K. Salem: conceptualization; data curation; formal analysis; software; investigation; methodology; validation; visualization; and writing–original draft. Ahmed Abokifa: conceptualization; funding acquisition; methodology; project administration; resources; supervision; and writing–review and editing.
References
Adedoja, O. S., Y. Hamam, B. Khalaf, and R. Sadiku. 2019. “Sensor placement strategies for contamination identification in water distribution networks: A review.” WIT Trans. Ecol. Environ. 229 (Jul): 79–90. https://doi.org/10.2495/WRM190081.
Afshar, A., and M. A. Mariño. 2012. “Multiobjective coverage-based ACO model for quality monitoring in large water networks.” Water Resour. Manage. 26 (Jun): 2159–2176. https://doi.org/10.1007/s-012-0008-2.
Aral, M. M., J. Guan, and M. L. Maslia. 2010. “Optimal design of sensor placement in water distribution networks.” J. Water Resour. Plann. Manage. 136 (1): 5–18. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000001.
Banik, B. K., L. Alfonso, C. Di Cristo, and A. Leopardi. 2017a. “Greedy algorithms for sensor location in sewer systems.” Water 9 (11): 856. https://doi.org/10.3390/w9110856.
Banik, B. K., L. Alfonso, C. Di Cristo, A. Leopardi, and A. Mynett. 2017b. “Evaluation of different formulations to optimally locate sensors in sewer systems.” J. Water Resour. Plann. Manage. 143 (7): 04017026. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000778.
Banik, B. K., L. Alfonso, A. S. Torres, A. Mynett, C. Di Cristo, and A. Leopardi. 2015. “Optimal placement of water quality monitoring stations in sewer systems: An information theory approach.” Procedia Eng. 119 (Mar): 1308–1317. https://doi.org/10.1016/j.proeng.2015.08.956.
Bartos, M., and B. Kerkez. 2021. “Observability-based sensor placement improves contaminant tracing in river networks.” Water Resour. Res. 57 (7): 1–20. https://doi.org/10.1029/2020WR029551.
Bivins, A., J. Greaves, R. Fischer, K. C. Yinda, W. Ahmed, M. Kitajima, V. J. Munster, and K. Bibby. 2020. “Persistence of SARS-CoV-2 in water and wastewater.” Environ. Sci. Technol. Lett. 7 (12): 937–942. https://doi.org/10.1021/acs.estlett.0c00730.
Bourgeois, W., J. E. Burgess, and R. M. Stuetz. 2001. “On-line monitoring of wastewater quality: A review.” J. Chem. Technol. Biotechnol. 76 (Jul): 337–348. https://doi.org/10.1002/jctb.393.
Brentan, B., S. Carpitella, D. Barros, G. Meirelles, A. Certa, and J. Izquierdo. 2021. “Water quality sensor placement: A multiobjective and multi-criteria approach.” Water Resour. Manage. 35 (Mar): 225–241. https://doi.org/10.1007/s11269-020-02720-3.
Butler, D., C. Digman, C. Makropoulos, and J. W. Davies. 2018. Urban drainage. 4th ed. Boca Raton, FL: CRC Press. https://doi.org/10.1201/9781351174305.
Calle, E., D. Martínez, R. Brugués-i-Pujolràs, M. Farreras, J. Saló-Grau, J. Pueyo-Ros, and L. Corominas. 2021. “Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks.” Environ. Int. 157 (Dec): 106768. https://doi.org/10.1016/j.envint.2021.106768.
Diaz-Fierros, T. F., J. Puerta, J. Suarez, and V. F. Diaz-Fierros. 2002. “Contaminant loads of CSOs at the wastewater treatment plant of a city in NW Spain.” Urban Water 4 (3): 291–299. https://doi.org/10.1016/S1462-0758(02)00020-1.
Edmondson, V., M. Cerny, M. Lim, B. Gledson, S. Lockley, and J. Woodward. 2018. “A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management.” Autom. Constr. 91 (Dec): 193–205. https://doi.org/10.1016/j.autcon.2018.03.003.
Elbeltagi, E., T. Hegazy, and D. Grierson. 2005. “Comparison among five evolutionary-based optimization algorithms.” Adv. Eng. Inf. 19 (1): 43–53. https://doi.org/10.1016/j.aei.2005.01.004.
Gad, A. F. 2024. “PyGAD: An intuitive genetic algorithm Python library.” Multimed. Tools Appl. 83: 58029–58042. https://doi.org/10.1007/s11042-023-17167-y.
Guadagno, V., G. Del Giudice, C. Di Cristo, A. Leopardi, and A. Simone. 2023. “Impact coefficient evaluation for sensor location in sewer systems.” J. Water Resour. Plann. Manage. 149 (11): 04023063. https://doi.org/10.1061/JWRMD5.WRENG-6093.
Hart, W. E., and R. Murray. 2010. “Review of sensor placement strategies for contamination warning systems in drinking water distribution systems.” J. Water Resour. Plann. Manage. 136 (6): 611–619. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000081.
Haupt, R. L., and S. E. Haupt. 2003. Practical genetic algorithms. New York: Wiley.
Huntington, D. E., and C. S. Lyrintzis. 1998. “Improvements to and limitations of Latin hypercube sampling.” Probab. Eng. Mech. 13 (Mar): 245–253. https://doi.org/10.1016/S0266-8920(97)00013-1.
Larson, R. C., O. Berman, and M. Nourinejad. 2020. “Sampling manholes to home in on SARS-CoV-2 infections.” PLoS One 15 (10): 1–20. https://doi.org/10.1371/journal.pone.0240007.
Lin, W., Z. Huang, S. Gao, Z. Luo, W. An, P. Li, S. Ping, and Y. Ren. 2021. “Evaluating the stability of prescription drugs in municipal wastewater and sewers based on wastewater-based epidemiology.” Sci. Total Environ. 754 (Feb): 142414. https://doi.org/10.1016/j.scitotenv.2020.142414.
McDonnell, B., K. Ratliff, M. Tryby, J. Wu, and A. Mullapudi. 2020. “PySWMM: The Python interface to stormwater management model (SWMM).” J. Open Source Software 5 (52): 2292. https://doi.org/10.21105/joss.02292.
Mu, T., M. Huang, S. Tang, R. Zhang, G. Chen, and B. Jiang. 2022. “Sensor partitioning placements via random walk and water quality and leakage detection models within water distribution systems.” Water Resour. Manage. 36 (May): 5297–5311. https://doi.org/10.1007/s11269-022-03312-z.
Nourinejad, M., O. Berman, and R. C. Larson. 2021. “Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus.” PLoS One 16 (Apr): e0248893. https://doi.org/10.1371/journal.pone.0248893.
Pan, T.-C., and J.-J. Kao. 2009. “GA-QP model to optimize sewer system design.” J. Environ. Eng. 135 (Apr): 17–24. https://doi.org/10.1061/(ASCE)0733-9372(2009)135:1(17).
Preis, A., and A. Ostfeld. 2008a. “Genetic algorithm for contaminant source characterization using imperfect sensors.” Civ. Eng. Environ. Syst. 25 (Dec): 29–39. https://doi.org/10.1080/10286600701695471.
Preis, A., and A. Ostfeld. 2008b. “Multiobjective contaminant response modeling for water distribution systems security.” J. Hydroinf. 10 (Mar): 267–274. https://doi.org/10.2166/hydro.2008.061.
Rathi, S., and R. Gupta. 2014. “Sensor placement methods for contamination detection in water distribution networks: A review.” Procedia Eng. 89 (Jan): 181–188. https://doi.org/10.1016/j.proeng.2014.11.175.
Rathi, S., and R. Gupta. 2016. “A simple sensor placement approach for regular monitoring and contamination detection in water distribution networks.” KSCE J. Civ. Eng. 20 (Mar): 597–608. https://doi.org/10.1007/s12205-015-0024-x.
Salem, A. K., and A. A. Abokifa. 2023. “Machine learning–based source identification in sewer networks.” J. Water Resour. Plann. Manage. 149 (8): 04023034. https://doi.org/10.1061/JWRMD5.WRENG-6050.
Sambito, M., C. Di Cristo, G. Freni, and A. Leopardi. 2020. “Optimal water quality sensor positioning in urban drainage systems for illicit intrusion identification.” J. Hydroinf. 22 (1): 46–60. https://doi.org/10.2166/hydro.2019.036.
Sambito, M., and G. Freni. 2021. “Strategies for improving optimal positioning of quality sensors in urban drainage systems for non-conservative contaminants.” Water 13 (7): 934. https://doi.org/10.3390/w13070934.
Taha, A. F., S. Wang, Y. Guo, T. H. Summers, N. Gatsis, M. H. Giacomoni, and A. A. Abokifa. 2021. “Revisiting the water quality sensor placement problem: Optimizing network observability and state estimation metrics.” J. Water Resour. Plann. Manage. 147 (7): 1–13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001374.
Tatiparthi, S. R., Y. G. De Costa, C. N. Whittaker, S. Hu, Z. Yuan, R. Y. Zhong, and W. Q. Zhuang. 2021. “Development of radio-frequency identification (RFID) sensors suitable for smart-monitoring applications in sewer systems.” Water Res. 198 (Jun): 117107. https://doi.org/10.1016/j.watres.2021.117107.
Wang, Y., C. L. Moe, S. Dutta, A. Wadhwa, S. Kanungo, W. Mairinger, Y. Zhao, Y. Jiang, and P. F. Teunis. 2020. “Designing a typhoid environmental surveillance study: A simulation model for optimum sampling site allocation.” Epidemics 31 (Jun): 100391. https://doi.org/10.1016/j.epidem.2020.100391.
Xuesong, Y., S. Jie, and H. Chengyu. 2017. “Research on contaminant sources identification of uncertainty water demand using genetic algorithm.” Cluster Comput. 20 (Jun): 1007–1016. https://doi.org/10.1007/s10586-017-0787-6.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 11 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 5, 2023
Accepted: Jun 3, 2024
Published online: Aug 26, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 26, 2025
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.