Evaluation of Different Formulations to Optimally Locate Sensors in Sewer Systems

Banik, Bijit Kumar; Alfonso, Leonardo; Cristo, Cristiana Di; Leopardi, Angelo; Mynett, Arthur E.

doi:10.1061/(asce)wr.1943-5452.0000778

Cited by 40 publications

(27 citation statements)

References 53 publications

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“…The performances of different multi-objective formulations, characterized by distinct objective functions and solved with the NSGA-II algorithm, are evaluated. In [25], a further comparison has been performed with a single-objective rank-based GR procedure, confirming the efficiency of this approach in finding the extreme Pareto solutions.…”

Section: Introductionmentioning

confidence: 90%

“…As more detailed description of the procedures, the data for evaluating the objectives were obtained by performing hydraulic and water-quality simulations using the well-known Storm Water Management Model (SWMM) software by USEPA (Environmental Protection Agency, USA). The proposed procedures are also compared with two procedures presented in [25], indicated as B_IT and B_DR, with the sensor location formulated as a multi-objective optimization problem solved using the Genetic Algorithm NSGA-II.…”

Section: Methodsologymentioning

confidence: 99%

“…New methodologies for locating sensors in sewer systems, formulated as rank-based GR optimization problems, are proposed. The improvement of this work with respect to the previous work by [25] is represented by the comparison of six different GR procedures: three single-objective formulations are compared with three multi-objective formulations. In fact, to the best of our knowledge, no study has been reported that considers multiple objectives in greedy optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, in [24,25], some methodologies for optimally designing a monitoring network in sewer systems have been proposed. The performances of different multi-objective formulations, characterized by distinct objective functions and solved with the NSGA-II algorithm, are evaluated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Greedy Algorithms for Sensor Location in Sewer Systems

Banik

Alfonso

Cristo

et al. 2017

Water

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Wastewater quality monitoring is receiving growing interest with the necessity of developing new strategies for controlling accidental and intentional illicit intrusions. In designing a monitoring network, a crucial aspect is represented by the sensors' location. In this study, a methodology for the optimal placement of wastewater monitoring sensors in sewer systems is presented. The sensor location is formulated as an optimization problem solved using greedy algorithms (GRs). The Storm Water Management Model (SWMM) was used to perform hydraulic and water-quality simulations. Six different procedures characterized by different fitness functions are presented and compared. The performances of the procedures are tested on a real sewer system, demonstrating the suitability of GRs for the sensor-placement problem. The results show a robustness of the methodology with respect to the detection concentration parameter, and they suggest that procedures with multiple objectives into a single fitness function give better results. A further comparison is performed using previously developed multi-objective procedures with multiple fitness functions solved using a genetic algorithm (GA), indicating better performances of the GR. The existing monitoring network, realized without the application of any sensor design, is always suboptimal.

show abstract

Section: Introductionmentioning

confidence: 90%

Section: Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Greedy Algorithms for Sensor Location in Sewer Systems

Banik

Alfonso

Cristo

et al. 2017

Water

Self Cite

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“…Lee [39] found that by maximizing the multivariate transinformation between chosen and unchosen stations, using the storm water management model to simulate the total suspended solids and a GA for optimization, an optimal water quality network could be designed for a sewer system. Banik et al [82] compared information theory, detection time and reliability measures for the design of a sewer system monitoring network through both single and multi-objective optimization approaches. It was shown that for a small monitoring network, the methods had similar performances, while the single objective detection time-based method had slightly better performance when the number of monitoring station is larger.…”

Section: Water Quality Networkmentioning

confidence: 99%

Entropy Applications to Water Monitoring Network Design: A Review

Keum

Kornelsen

Leach

et al. 2017

Entropy

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Abstract:Having reliable water monitoring networks is an essential component of water resources and environmental management. A standardized process for the design of water monitoring networks does not exist with the exception of the World Meteorological Organization (WMO) general guidelines about the minimum network density. While one of the major challenges in the design of optimal hydrometric networks has been establishing design objectives, information theory has been successfully adopted to network design problems by providing measures of the information content that can be deliverable from a station or a network. This review firstly summarizes the common entropy terms that have been used in water monitoring network designs. Then, this paper deals with the recent applications of the entropy concept for water monitoring network designs, which are categorized into (1) precipitation; (2) streamflow and water level; (3) water quality; and (4) soil moisture and groundwater networks. The integrated design method for multivariate monitoring networks is also covered. Despite several issues, entropy theory has been well suited to water monitoring network design. However, further work is still required to provide design standards and guidelines for operational use.

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Citizen Science and Low-Cost Sensors for Integrated Water Resources Management

Paul

Buytaert

2018

Advanced Tools for Integrated Water Resources Management

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Evaluation of Different Formulations to Optimally Locate Sensors in Sewer Systems

Cited by 40 publications

References 53 publications

Greedy Algorithms for Sensor Location in Sewer Systems

Greedy Algorithms for Sensor Location in Sewer Systems

Entropy Applications to Water Monitoring Network Design: A Review

Citizen Science and Low-Cost Sensors for Integrated Water Resources Management

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