deprecate the use of this approach based on that critique alone. Having said this, a
further concern relevant to the use of clustering algorithms on their own may be the
fact that the majority of optimally located pressure sensors in a network tend to
detect the same set of leaks/bursts (i.e. the observation by [52], already mentioned
above). In this regard, it could be argued that clustering algorithms, if used on their
own, may struggle to provide information useful for enabling efficient leak/burst
localisation.
Some of the optimal sensor placement methods that can be found in the literature
utilise a different method for determining the instrumentation locations and for
localising leaks/bursts (e.g. [11, 34, 91]). It is clear that this approach implies that
the resulting sensor placements will not be optimised for the chosen method of leak/
burst localisation. Therefore, it is envisaged that tightly coupled optimal sensor
placement and leak/burst localisation frameworks should be developed by
researchers in the future. Leak/burst localisation and sensor placement should be
considered together since the best placement depends on the method that is used to
localise the potential leaks/bursts and the efficiency of the leak/burst localisation
depends on the sensor placement.
Much greater attention should be paid in the future to the issue of sensor/
communication failures as this is of critical importance for the effectiveness of
optimal sensor placements for leak/burst detection and localisation. In Sect. 2, it
was noted that the use of the method proposed by Farley et al. [11, 34] would make
the task of correctly localising a leak/burst impossible if a single sensor is not
working or data are not timely received. Bearing this in mind, similar considerations
could be made for the majority of the reviewed optimal sensor placement methods
for leak/burst detection and localisation as they have been developed under the
unrealistic assumption that all the sensors perform without any failure at all times.
In this context, the methodology proposed by Boatwright et al. [63] may offer a
potentially appealing way to mitigate the issue under scrutiny. Indeed, geostatistical
interpolation techniques are less reliant on the availability of data from all the
optimally deployed sensors when performing leak/burst localisation than methods
that are based on assessing the similarities between the observed residuals and the
results of hydraulic simulations, for example. Based on similar arguments, it may be
possible to state that the use of geostatistical interpolation techniques could also be
beneficial for mitigating some of the issues that arise because of model and measurements uncertainties. Indeed, a resulting interpolation surface created by using
observed pressure measurements (which provides inferred values of pressure at
every point in a network) attempts to mimic the results of a hydraulic simulation
but without the reliance on an accurate hydraulic model/good measurements fed into
a hydraulic model.
Methods for the optimal placement of pressure and flow sensors simultaneously
should also be the focus of further research and development in the future. This is
because early studies (e.g. [91]) have indicated that using additional flow instrumentation can improve the leak/burst detection and localisation performance of
optimal sensor placement methods that only use additional pressure sensors.
Due to the higher costs associated with obtaining flow measurements, however,
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
53
further concern relevant to the use of clustering algorithms on their own may be the
fact that the majority of optimally located pressure sensors in a network tend to
detect the same set of leaks/bursts (i.e. the observation by [52], already mentioned
above). In this regard, it could be argued that clustering algorithms, if used on their
own, may struggle to provide information useful for enabling efficient leak/burst
localisation.
Some of the optimal sensor placement methods that can be found in the literature
utilise a different method for determining the instrumentation locations and for
localising leaks/bursts (e.g. [11, 34, 91]). It is clear that this approach implies that
the resulting sensor placements will not be optimised for the chosen method of leak/
burst localisation. Therefore, it is envisaged that tightly coupled optimal sensor
placement and leak/burst localisation frameworks should be developed by
researchers in the future. Leak/burst localisation and sensor placement should be
considered together since the best placement depends on the method that is used to
localise the potential leaks/bursts and the efficiency of the leak/burst localisation
depends on the sensor placement.
Much greater attention should be paid in the future to the issue of sensor/
communication failures as this is of critical importance for the effectiveness of
optimal sensor placements for leak/burst detection and localisation. In Sect. 2, it
was noted that the use of the method proposed by Farley et al. [11, 34] would make
the task of correctly localising a leak/burst impossible if a single sensor is not
working or data are not timely received. Bearing this in mind, similar considerations
could be made for the majority of the reviewed optimal sensor placement methods
for leak/burst detection and localisation as they have been developed under the
unrealistic assumption that all the sensors perform without any failure at all times.
In this context, the methodology proposed by Boatwright et al. [63] may offer a
potentially appealing way to mitigate the issue under scrutiny. Indeed, geostatistical
interpolation techniques are less reliant on the availability of data from all the
optimally deployed sensors when performing leak/burst localisation than methods
that are based on assessing the similarities between the observed residuals and the
results of hydraulic simulations, for example. Based on similar arguments, it may be
possible to state that the use of geostatistical interpolation techniques could also be
beneficial for mitigating some of the issues that arise because of model and measurements uncertainties. Indeed, a resulting interpolation surface created by using
observed pressure measurements (which provides inferred values of pressure at
every point in a network) attempts to mimic the results of a hydraulic simulation
but without the reliance on an accurate hydraulic model/good measurements fed into
a hydraulic model.
Methods for the optimal placement of pressure and flow sensors simultaneously
should also be the focus of further research and development in the future. This is
because early studies (e.g. [91]) have indicated that using additional flow instrumentation can improve the leak/burst detection and localisation performance of
optimal sensor placement methods that only use additional pressure sensors.
Due to the higher costs associated with obtaining flow measurements, however,
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
53