these methods could/should also account for the differences in costs and budget
constraints and balance all this with the additional benefits that could be realised.
It is envisaged that optimal sensor placement methods should also account for
risk as, while detecting and localising all leaks/bursts is important, not all leaks/
bursts are equally impactful. Nowadays, the resulting potential unplanned interruptions to the water supply and the damaging consequences of the leak/burst events are
tolerated to a lesser extent, and water companies are increasingly judged by the
public (and the regulatory agencies alike, where applicable) based on how well
(or otherwise) they manage contingency situations. In this context, future work on
this subject should focus on further refining the means of estimating the likelihood
and impact components of risk.
The literature review carried out in this chapter has highlighted that different
methods, the inclusion of different objectives in similar methods and even slightly
changing specific settings within the same method (e.g. incorporating different
strengths of uncertainty) lead to significantly different sensor placements. Despite
measures for the assessment of performance being found in the literature, the
majority of these measures are tailored to the particular method being proposed.
All this makes the task of assessing whether a sensor placement is better that another
sensor placement almost impossible. Bearing this in mind, optimal sensor placement
research is pressingly in need for field trials and validation, which are the only way to
understand the real value and practicality of a proposed approach. In the relevant
literature, only the studies by Farley et al. [11, 33, 34] and Fuchs-Hanusch and
Steffelbauer [97] report the results of field trials and validations. The main findings
from the field trials carried out in the studies by Farley et al. [11, 33, 34] have been
detailed in Sect. 2. These findings demonstrated the practical applicability of the
methods proposed in those studies. On the other hand, in Fuchs-Hanusch and
Steffelbauer [97], a comparison of several methods including the methods proposed
by Pérez et al. [31], Casillas et al. [39] and Steffelbauer and Fuchs-Hanusch [78] was
carried out by opening fire hydrants to simulate different leak/burst scenarios in a
real network and then assessing the leak/burst localisation capabilities of the different methods by calculating the distance between the suggested leak/burst locations
and the opened fire hydrants. The results from the limited tests carried out in that
study showed that for different leak/burst positions, different sensor sets, mainly
those with sensors close to the leak/burst position, led to the best performance. These
quite disappointing findings cast a shadow on the real value of the various “optimal”
sensor placement methods that have been proposed so far and demonstrated using
numerical simulations only, therefore stressing even more the need for any future
optimal sensor placement study to be thoroughly field validated in real-life networks.
It is envisaged that, as a bare minimum, future optimal sensor placement studies
should include an assessment of their underlying capabilities using a set of common
quantitative metrics which may include/take inspiration by those recently proposed
by Qi et al. [98]. In this context, the use by researchers in the field of a common set of
benchmark models that cover a range of network layouts/sizes/etc. and a common set
of leak/burst scenarios could also be beneficial.
54
M. Romano
constraints and balance all this with the additional benefits that could be realised.
It is envisaged that optimal sensor placement methods should also account for
risk as, while detecting and localising all leaks/bursts is important, not all leaks/
bursts are equally impactful. Nowadays, the resulting potential unplanned interruptions to the water supply and the damaging consequences of the leak/burst events are
tolerated to a lesser extent, and water companies are increasingly judged by the
public (and the regulatory agencies alike, where applicable) based on how well
(or otherwise) they manage contingency situations. In this context, future work on
this subject should focus on further refining the means of estimating the likelihood
and impact components of risk.
The literature review carried out in this chapter has highlighted that different
methods, the inclusion of different objectives in similar methods and even slightly
changing specific settings within the same method (e.g. incorporating different
strengths of uncertainty) lead to significantly different sensor placements. Despite
measures for the assessment of performance being found in the literature, the
majority of these measures are tailored to the particular method being proposed.
All this makes the task of assessing whether a sensor placement is better that another
sensor placement almost impossible. Bearing this in mind, optimal sensor placement
research is pressingly in need for field trials and validation, which are the only way to
understand the real value and practicality of a proposed approach. In the relevant
literature, only the studies by Farley et al. [11, 33, 34] and Fuchs-Hanusch and
Steffelbauer [97] report the results of field trials and validations. The main findings
from the field trials carried out in the studies by Farley et al. [11, 33, 34] have been
detailed in Sect. 2. These findings demonstrated the practical applicability of the
methods proposed in those studies. On the other hand, in Fuchs-Hanusch and
Steffelbauer [97], a comparison of several methods including the methods proposed
by Pérez et al. [31], Casillas et al. [39] and Steffelbauer and Fuchs-Hanusch [78] was
carried out by opening fire hydrants to simulate different leak/burst scenarios in a
real network and then assessing the leak/burst localisation capabilities of the different methods by calculating the distance between the suggested leak/burst locations
and the opened fire hydrants. The results from the limited tests carried out in that
study showed that for different leak/burst positions, different sensor sets, mainly
those with sensors close to the leak/burst position, led to the best performance. These
quite disappointing findings cast a shadow on the real value of the various “optimal”
sensor placement methods that have been proposed so far and demonstrated using
numerical simulations only, therefore stressing even more the need for any future
optimal sensor placement study to be thoroughly field validated in real-life networks.
It is envisaged that, as a bare minimum, future optimal sensor placement studies
should include an assessment of their underlying capabilities using a set of common
quantitative metrics which may include/take inspiration by those recently proposed
by Qi et al. [98]. In this context, the use by researchers in the field of a common set of
benchmark models that cover a range of network layouts/sizes/etc. and a common set
of leak/burst scenarios could also be beneficial.
54
M. Romano