The robust sensor placement methodology has been applied to the real WDN in Barcelona (see section ‘Case study 2 description’). Step 1 produces the same reduced candidate sensor set displayed in Figure 5. Next, step 2 is applied. Figure 8 displays the resulting Pareto front on the objective space as black circles, and Table 4 provides the eight Pareto optimal solutions found.
Table 4

Pareto optimal solutions for case study 2

 {199,222,243,244,285}{171,199,222,243,285}{8,199,222,243,285}{9,199,222,243,285}
 38,927 42,230 46,738 46,738 
 73,194 72,751 70,580 70,580 
{8,171,199,222,243}{9,171,199,222,243}{8,171,199,222,285}{9,171,199,222,285}
 48,713 48,713 48,845 48,845 
 70,027 70,027 69,523 69,523 
 {199,222,243,244,285}{171,199,222,243,285}{8,199,222,243,285}{9,199,222,243,285}
 38,927 42,230 46,738 46,738 
 73,194 72,751 70,580 70,580 
{8,171,199,222,243}{9,171,199,222,243}{8,171,199,222,285}{9,171,199,222,285}
 48,713 48,713 48,845 48,845 
 70,027 70,027 69,523 69,523 
Figure 8

Feasible solutions (grey dots) and Pareto front (black circles) on the objective space for case study 2.

Figure 8

Feasible solutions (grey dots) and Pareto front (black circles) on the objective space for case study 2.

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