simultaneously use pressure and flow sensors for leak/burst detection and, most
importantly, localisation is needed. However, despite what stated at the beginning of
this section with regard to costs, these methods could/should be further developed to
also account for the differences in costs and budget constraints. Bearing this in mind,
accounting for differences in costs has been attempted in studies such as those
presented in Candelieri et al. [59] and Jung and Kim [93], for example, but a more
thorough analysis framework for dealing with these issues would be beneficial to
water companies.
3.5 Accounting for Risk
A potential drawback of all the optimal sensor placement approaches reviewed so far
is that they tend to treat all leaks/bursts in the network equally – i.e. without
considering the potential impact they may have on customers, for example. In
real-life circumstances, a water company may decide to favour sensor placements
that ensure quick detection and localisation of events that may have a major impact
on nearby customers (e.g. cause local road or property damage) and especially if the
customers in question are sensitive/critical (e.g. hospitals).
In Forconi et al. [83], three different risk-based functions were used to derive
optimal placements of a given number of sensors in a WDS: a simple function based
on likelihood of leak/burst non-detection and two other risk-based functions, where
impact and exposure/vulnerability are combined with the leak/burst detection likelihood. The impact is measured by the effects of a leak/burst occurrence on the
demands (i.e. volume of undelivered water), while the exposure/vulnerability is
measured by the intrinsic importance of the elements that can be damaged
(by assigning higher weights to certain nodes). This method therefore enables to
take into account social, economic and/or safety considerations. The results obtained
showed that accounting for risk can lead to significantly different sensor placements.
In this context, the methodology proposed in this study can represent a useful tool for
the WDS’s managers for placing sensors in the network in order to not only detect
and localise leaks/bursts but to also comply with hydraulic, social and economic
requirements.
Venkateswaran et al. [94] presented a good example of work that focus on
refining the means of estimating the likelihood and impact components of risk
(i.e. one of the most important issues in risk-based approaches). In that study, the
authors proposed an approach to model and quantify the real-world impact of a leak/
burst event on a community using various geospatial, infrastructural and societal
factors. Specifically, they modelled the vulnerability of a community to flooding by
simulating the propagation of water from a leak/burst along the surrounding terrain
using a hydrodynamic flood simulation algorithm. They also partitioned the community into regions (driven by flood maps, which depend on the terrain) and
determined the relative criticality of these regions by assigning scores based on the
population density as well as the critical infrastructure (e.g. healthcare,
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
45
importantly, localisation is needed. However, despite what stated at the beginning of
this section with regard to costs, these methods could/should be further developed to
also account for the differences in costs and budget constraints. Bearing this in mind,
accounting for differences in costs has been attempted in studies such as those
presented in Candelieri et al. [59] and Jung and Kim [93], for example, but a more
thorough analysis framework for dealing with these issues would be beneficial to
water companies.
3.5 Accounting for Risk
A potential drawback of all the optimal sensor placement approaches reviewed so far
is that they tend to treat all leaks/bursts in the network equally – i.e. without
considering the potential impact they may have on customers, for example. In
real-life circumstances, a water company may decide to favour sensor placements
that ensure quick detection and localisation of events that may have a major impact
on nearby customers (e.g. cause local road or property damage) and especially if the
customers in question are sensitive/critical (e.g. hospitals).
In Forconi et al. [83], three different risk-based functions were used to derive
optimal placements of a given number of sensors in a WDS: a simple function based
on likelihood of leak/burst non-detection and two other risk-based functions, where
impact and exposure/vulnerability are combined with the leak/burst detection likelihood. The impact is measured by the effects of a leak/burst occurrence on the
demands (i.e. volume of undelivered water), while the exposure/vulnerability is
measured by the intrinsic importance of the elements that can be damaged
(by assigning higher weights to certain nodes). This method therefore enables to
take into account social, economic and/or safety considerations. The results obtained
showed that accounting for risk can lead to significantly different sensor placements.
In this context, the methodology proposed in this study can represent a useful tool for
the WDS’s managers for placing sensors in the network in order to not only detect
and localise leaks/bursts but to also comply with hydraulic, social and economic
requirements.
Venkateswaran et al. [94] presented a good example of work that focus on
refining the means of estimating the likelihood and impact components of risk
(i.e. one of the most important issues in risk-based approaches). In that study, the
authors proposed an approach to model and quantify the real-world impact of a leak/
burst event on a community using various geospatial, infrastructural and societal
factors. Specifically, they modelled the vulnerability of a community to flooding by
simulating the propagation of water from a leak/burst along the surrounding terrain
using a hydrodynamic flood simulation algorithm. They also partitioned the community into regions (driven by flood maps, which depend on the terrain) and
determined the relative criticality of these regions by assigning scores based on the
population density as well as the critical infrastructure (e.g. healthcare,
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
45