shorter average detection time is more favourable) detection efficiency indicator.
With specific regard to the issue of false alarms, the authors found that maximising
the rate of correct event detections and minimising the rate of false alarms are
contradictory goals and the best detection locations are not likely to be the best
locations for minimising the rate of false alarms. Furthermore, they found that, as the
number of sensors in the DMA increases, both the rate of correct event detections
and the rate of false alarms increase.
Huang et al. [57] developed a clustering-based pressure sensor placement
method for pipe burst detection. They used a fuzzy self-organising map neural
network (see – e.g. [58]) due to its capability to classify the inputs without knowing
the number of clusters in advance and, hence, with the capability of enabling to
determine the optimal number of pressure sensors required. In this method, the nodes
in a WDS/DMA are grouped according to their similarity in responding to the
change of node demands due to leaks/bursts. A small real-world network was used
to demonstrate the effectiveness of the method. Here, the authors simulated leaks/
bursts as a single, constant demand, considered a single time step (i.e. the hour of
maximum daily water consumption), assumed the availability of a perfect hydraulic
model and did not account for measurements noise. Because of the limited verification of the methodology carried out in this study, it is difficult to assess the value of
the proposed method. Bearing this in mind, it is also important to stress that the
authors stated that setting the parameters of the self-organising map neural network
properly is not a trivial task and further investigations into this issue are required if
this method is to be used by water companies.
Candelieri et al. [59] proposed a method that makes use of (1) a graph-based,
spectral clustering procedure (see – e.g. [60]) of similar variations in pressure and
flow induced by leaks/bursts simulated using a hydraulic model and (2) support
vector machines classification (see – e.g. [61]) to learn the relationship between the
variations in pressure and flow at the deployed sensor locations and the most
probable set of pipes affected by a leak/burst (i.e. to learn to approximate the
non-linear mapping performed by the spectral clustering procedure and estimate
the most probable cluster which an “actual” vector of variations in pressure and flow
would belong to). They run several leak/burst scenarios by varying leak/burst
location and magnitude, assuming the availability of a perfect model and perfect
sensors’ measurements. They proposed to use a “localisation index” measure [62]
and a novel “quality of localisation” measure to evaluate the quality of the identified
clusters. The authors looked at the simultaneous deployment of pressure and flow
sensors by introducing a simple measure of cost (i.e. the cost of a flow sensor is ten
times the cost of a pressure sensor). They demonstrated the capabilities of their
method by applying it to the study of the optimal sensor locations for a real-life
DMA in Timisoara, Romania.
Boatwright et al. [63] proposed a novel combined sensor placement – leak/burst
localisation methodology based upon a spatially constrained version of the inverse
distance weighted geospatial interpolation technique (see [64]) that aims at ensuring
that optimal sensor locations (with respect to the leak/burst localisation
technique used) are selected. The proposed methodology makes use of the
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
37
With specific regard to the issue of false alarms, the authors found that maximising
the rate of correct event detections and minimising the rate of false alarms are
contradictory goals and the best detection locations are not likely to be the best
locations for minimising the rate of false alarms. Furthermore, they found that, as the
number of sensors in the DMA increases, both the rate of correct event detections
and the rate of false alarms increase.
Huang et al. [57] developed a clustering-based pressure sensor placement
method for pipe burst detection. They used a fuzzy self-organising map neural
network (see – e.g. [58]) due to its capability to classify the inputs without knowing
the number of clusters in advance and, hence, with the capability of enabling to
determine the optimal number of pressure sensors required. In this method, the nodes
in a WDS/DMA are grouped according to their similarity in responding to the
change of node demands due to leaks/bursts. A small real-world network was used
to demonstrate the effectiveness of the method. Here, the authors simulated leaks/
bursts as a single, constant demand, considered a single time step (i.e. the hour of
maximum daily water consumption), assumed the availability of a perfect hydraulic
model and did not account for measurements noise. Because of the limited verification of the methodology carried out in this study, it is difficult to assess the value of
the proposed method. Bearing this in mind, it is also important to stress that the
authors stated that setting the parameters of the self-organising map neural network
properly is not a trivial task and further investigations into this issue are required if
this method is to be used by water companies.
Candelieri et al. [59] proposed a method that makes use of (1) a graph-based,
spectral clustering procedure (see – e.g. [60]) of similar variations in pressure and
flow induced by leaks/bursts simulated using a hydraulic model and (2) support
vector machines classification (see – e.g. [61]) to learn the relationship between the
variations in pressure and flow at the deployed sensor locations and the most
probable set of pipes affected by a leak/burst (i.e. to learn to approximate the
non-linear mapping performed by the spectral clustering procedure and estimate
the most probable cluster which an “actual” vector of variations in pressure and flow
would belong to). They run several leak/burst scenarios by varying leak/burst
location and magnitude, assuming the availability of a perfect model and perfect
sensors’ measurements. They proposed to use a “localisation index” measure [62]
and a novel “quality of localisation” measure to evaluate the quality of the identified
clusters. The authors looked at the simultaneous deployment of pressure and flow
sensors by introducing a simple measure of cost (i.e. the cost of a flow sensor is ten
times the cost of a pressure sensor). They demonstrated the capabilities of their
method by applying it to the study of the optimal sensor locations for a real-life
DMA in Timisoara, Romania.
Boatwright et al. [63] proposed a novel combined sensor placement – leak/burst
localisation methodology based upon a spatially constrained version of the inverse
distance weighted geospatial interpolation technique (see [64]) that aims at ensuring
that optimal sensor locations (with respect to the leak/burst localisation
technique used) are selected. The proposed methodology makes use of the
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
37