Prioritizing monitoring locations in a water distribution network: a fuzzy risk approach

Francisque, Alex; Rodriguez, Manuel J.; Sadiq, Rehan; Miranda, Luis F.; Proulx, François

doi:10.2166/aqua.2009.011

Cited by 38 publications

(16 citation statements)

References 13 publications

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“…There are certain constraints to the proposed methodology, most of all its already mentioned subjectivity. This could have been circumvented by application of a fuzzy logic approach, as has been done for monitoring network design in fields of water quality in catchments (Strobl et al ., , ) and water distribution systems (Francisque et al ., ) before. This would have led to higher reproducibility, but since the fuzzification (i.e.…”

Section: Discussionmentioning

confidence: 96%

“…Temporary monitoring network. See Figure 6 for location.This figure is available in colour online at wileyonlinelibrary.com/journal/ird water quality in catchments (Strobl et al, 2006a(Strobl et al, , 2006b) and water distribution systems (Francisque et al, 2009) before. This would have led to higher reproducibility, but since the fuzzification (i.e.…”

Section: Discussionmentioning

confidence: 99%

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A Guideline for Developing an Initial Hydrological Monitoring Network as a Basis for Water Management in Artificially Drained Wetlands

Fahle

Dietrich

Lischeid

2013

Irrigation and Drainage

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Reliable hydrological monitoring is the basis for sound water management in drained wetlands. Since statistical methods cannot be employed for unobserved or sparsely monitored areas, the primary design (first set‐up) may be arbitrary in most instances. The objective of this paper is therefore to provide a guideline for designing the initial hydrological monitoring network. A scheme is developed that handles different parts of monitoring and hydrometry in wetlands, focusing on the positioning of surface water and groundwater gauges. For placement of the former, control units are used which correspond to areas whose water levels can be regulated separately. The latter are arranged depending on hydrological response units, defined by combinations of soil type and land use, and the chosen surface water monitoring sites. A practical application of the approach is shown for an investigation area in the Spreewald region in north‐east Germany. The presented scheme leaves a certain degree of freedom to its user, allowing the inclusion of expert knowledge or special concerns. Based on easily obtainable data, the developed hydrological network serves as a first step in the iterative procedure of monitoring network optimisation. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

A Guideline for Developing an Initial Hydrological Monitoring Network as a Basis for Water Management in Artificially Drained Wetlands

Fahle

Dietrich

Lischeid

2013

Irrigation and Drainage

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“…The purpose of this section is to design and test an expert system for fusion of all the data acquired from the multiparameter monitoring systems. We focus on the parameters that demonstrate an ordinal relationship between the estimated water quality and the parameter values as addressed by Francisque et al 71 The set of two indicators including heterotrophic plate count (HPC) bacteria and water turbidity (Turb) are selected as microbial measures. An increase in water turbidity can provide a surrogate for the possible microbial contamination.…”

Section: A Water Quality Case Studymentioning

confidence: 99%

“…71 The average value of parameters in a season at one sampling point will be used to test the proposed algorithm. Two scenarios of expert information will be considered.…”

Section: A Water Quality Case Studymentioning

confidence: 99%

Evidential reasoning using extended fuzzy Dempster-Shafer theory for handling various facets of information deficiency

Aminravan

Sadiq

Hoorfar

et al. 2011

Int. J. Intell. Syst.

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2École supérieure d'aménagement du territoire et de développement régional, Université Laval, Quebec City, QC G1V 0A6, CanadaThis work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster-Shafer scheme based on the simultaneous use of fuzzy interval-grade and interval-valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster-Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. C 2011 Wiley Periodicals, Inc.

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“…Fuzzy relational systems are also used to model the behaviour of complex systems (Gardner and Dorling 1998). In the field of water quality monitoring, fuzzy rule-based systems (FRBSs) have been previously proposed for prioritizing the monitoring locations in a WDN (Francisque et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Application of neuro-fuzzy based expert system in water quality assessment

Aghaarabi

Aminravan

Sadiq

et al. 2014

Int J Syst Assur Eng Manag

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In this research a framework is developed to predict the drinking water quality through the neural network models. A fuzzy rule-based system and similarity measure algorithm yield a water quality index for different sampling locations in a water distribution network (WDN), and a neural network is trained using the quality indices. Different sources of uncertainty exist in this model, including deficient, missing, and noisy data, conflicting water quality parameters and subjective information. Hourly and monthly data from Quebec City WDN are used to illustrate the performance of the proposed neuro-fuzzy model. Also, historical data from 52 sampling locations of Quebec City network is utilized to develop the rule-based model in order to train the neural network. Water quality is evaluated by categorizing quality parameters in two groups including microbial and physicochemical. Two sets of rules are defined using expert knowledge to assign water quality grades to each sampling location in the WDN. The fuzzy inference system outputs are deffuzzified using a similarity measure algorithm in this approach. The fuzzy inference system acts as a decision making agent. A utility function provides microbial and physicochemical water quality indices. Final results are used to train the neural network. In the proposed framework, microbial and physicochemical quality of water are predicted individually.

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Prioritizing monitoring locations in a water distribution network: a fuzzy risk approach

Cited by 38 publications

References 13 publications

A Guideline for Developing an Initial Hydrological Monitoring Network as a Basis for Water Management in Artificially Drained Wetlands

A Guideline for Developing an Initial Hydrological Monitoring Network as a Basis for Water Management in Artificially Drained Wetlands

Evidential reasoning using extended fuzzy Dempster-Shafer theory for handling various facets of information deficiency

Application of neuro-fuzzy based expert system in water quality assessment

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