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- W4377028236 abstract "Wastewater surveillance has received significant attention in recent years due to the valuable environmental and epidemiological information it reveals. Yet, limited attempts have been made in the previous literature to determine the best locations for sample collection. In this study, we are introducing a model to solve the sensor placement (SP) problem that considers source identification (SI) as the main criterion for sensor placement. The objective of this study is to maximize the ability of the sensor design to identify the characteristics of the injection source (i.e., the injection location and concentration) of any given species of interest. To that end, we developed a python-based model that couples an SI model with a greedy algorithm to determine the best locations to place water quality sensors. The developed SP model was demonstrated on a benchmark, mid-sized sewer network. The study results revealed that a clear tradeoff exists between the overall identifiability and the detection reliability of the sensor design. In general, placing the sensors in the downstream end of the network resulted in higher overall identifiability, while better detection reliability was achieved by the sensors placed in the upstream section of the network." @default.
- W4377028236 created "2023-05-19" @default.
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- W4377028236 date "2023-05-18" @default.
- W4377028236 modified "2023-09-29" @default.
- W4377028236 title "Optimization of Water Quality Sensor Placement in Sewer Networks" @default.
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- W4377028236 doi "https://doi.org/10.1061/9780784484852.102" @default.
- W4377028236 hasPublicationYear "2023" @default.
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