Ricerc@Sapienza

Knowledge of the pressure field in water distribution networks (WDN) is important because it generally drives the operational actions for leakage and failure management, backwater intrusion and demand control. This knowledge would ideally be achieved by monitoring the pressure at each junction of the network. However, due to limited economic resources, only a small number of nodes can be controlled. Therefore, in order to obtain complete information on the pressure field while containing monitoring costs, two different steps will be followed in this research. First, several optimal sampling design methods will be studied, in particular entropy-based methods and methods based on the variance-covariance uncertainty matrix. In both cases, the optimal solution will be sought through single-objective (SOGA) or multi-objective (MOGA) genetic algorithms models. Second, the pressure values of the optimal groups (i.e. the optimal solutions from sampling design) will be used as input of an ANN which will generate as output the pressure values at all the other non-monitored nodes of the system. In sampling design and ANN training a pressure-driven hydraulic model with demand scenarios [ ] derived from the scaling laws of water consumptions [] will be used.