transportation, government facilities, educational, etc.) present within each region.
The sensor placement algorithms they developed, however, are greedy type
(i.e. algorithms that solve the problem by placing one sensor and find the next sensor
position through incorporation of the previous one), which have been shown to be
likely to fail in finding optimal sensor placements [26, 78].
4 Discussion
Based on the literature review carried out in the previous two sections, it is possible
to state that the various optimal sensor placement techniques that have been proposed by researchers have many differences but also similarities. Some studies have
focused on leak/burst detection only, while others have considered both leak/burst
detection and localisation. The optimal sensor placement problem has been formulated in a number of different ways, and the proposed solutions to the problem have
involved the use of different tools such as different hydraulic solvers and different
optimisation algorithms. Even when the same hydraulic solver is used, different
modelling approaches have been taken by researchers such as accounting for the
pressure-driven behaviour of a network or not, performing extended period or single
period simulations and simulating the occurrence of leaks/bursts by using additional
demands at nodes or emitters at nodes (or on pipes). Furthermore, some studies have
attempted to deal with one or more sources of uncertainty such as demand and
measurements uncertainty, while others have assumed the availability of a perfect
model and measurements, among other things. All the proposed techniques have
been tested and demonstrated on one or more case study networks. The characteristics of such case studies vary widely from small synthetic benchmark networks to
real-life DMAs in various parts of the world. Tests and demonstrations of the
proposed techniques have often involved numerical experiments only, but in some
cases field tests have also been carried out. Bearing this in mind, Tables 1, 2, 3 and 4
summarise the main characteristics of a number of selected publications that have
been reviewed in Sects. 2 and 3.
By scrutinising these tables and in the light of the literature review carried out in
Sects. 2 and 3, a number of considerations regarding, inter alia, the state of the art of
optimal sensor placement techniques, the potential of these techniques to help water
companies minimising the leaks/bursts’ runtime by effectively detecting and
localising these events as they occur in a DMA and the gaps in the current research
can be made. These considerations are detailed below.
Notwithstanding the individual contributions to the body of knowledge in the
field made by studies that have focused on optimal placement of pressure sensors for
leak/burst detection only, it is possible to observe that such studies have limited
value for water companies when considering the aim of minimising the leaks/bursts’
runtime. Indeed, studies such as Hagos et al. [52] noted that the majority of optimally
located pressure sensors in a network tend to detect the same set of leaks/bursts and,
thus, they provide little information on where a leak/burst may be located.
46
M. Romano
The sensor placement algorithms they developed, however, are greedy type
(i.e. algorithms that solve the problem by placing one sensor and find the next sensor
position through incorporation of the previous one), which have been shown to be
likely to fail in finding optimal sensor placements [26, 78].
4 Discussion
Based on the literature review carried out in the previous two sections, it is possible
to state that the various optimal sensor placement techniques that have been proposed by researchers have many differences but also similarities. Some studies have
focused on leak/burst detection only, while others have considered both leak/burst
detection and localisation. The optimal sensor placement problem has been formulated in a number of different ways, and the proposed solutions to the problem have
involved the use of different tools such as different hydraulic solvers and different
optimisation algorithms. Even when the same hydraulic solver is used, different
modelling approaches have been taken by researchers such as accounting for the
pressure-driven behaviour of a network or not, performing extended period or single
period simulations and simulating the occurrence of leaks/bursts by using additional
demands at nodes or emitters at nodes (or on pipes). Furthermore, some studies have
attempted to deal with one or more sources of uncertainty such as demand and
measurements uncertainty, while others have assumed the availability of a perfect
model and measurements, among other things. All the proposed techniques have
been tested and demonstrated on one or more case study networks. The characteristics of such case studies vary widely from small synthetic benchmark networks to
real-life DMAs in various parts of the world. Tests and demonstrations of the
proposed techniques have often involved numerical experiments only, but in some
cases field tests have also been carried out. Bearing this in mind, Tables 1, 2, 3 and 4
summarise the main characteristics of a number of selected publications that have
been reviewed in Sects. 2 and 3.
By scrutinising these tables and in the light of the literature review carried out in
Sects. 2 and 3, a number of considerations regarding, inter alia, the state of the art of
optimal sensor placement techniques, the potential of these techniques to help water
companies minimising the leaks/bursts’ runtime by effectively detecting and
localising these events as they occur in a DMA and the gaps in the current research
can be made. These considerations are detailed below.
Notwithstanding the individual contributions to the body of knowledge in the
field made by studies that have focused on optimal placement of pressure sensors for
leak/burst detection only, it is possible to observe that such studies have limited
value for water companies when considering the aim of minimising the leaks/bursts’
runtime. Indeed, studies such as Hagos et al. [52] noted that the majority of optimally
located pressure sensors in a network tend to detect the same set of leaks/bursts and,
thus, they provide little information on where a leak/burst may be located.
46
M. Romano