Abstract:
AbstractA contaminant source identification (CSI) methodology for water distribution systems is intended to identify possible events (i.e.,intrusion nodes, times, duration, and mass rate). The methodology has to be both rapid and able to incorporate uncertainties when identifying possible intrusion nodes (PINs). Identification of PINs has two major issues: the false-negative rate (failure to identify the true ingress location) and the false-positive issue (falsely identifying a location that is not the true ingress location). A data-mining procedure is described and applied, which involves mining an offline-built database to select PINs that possess first-detection times within Β±m from the online sensor first-detection time, with m selected to address issues of false negatives and positives. This data-mining approach is made possible through the power of parallel computing, which demonstrates huge potential by simulating scenarios simultaneously. In the case studies, scenario simulation times are reduc...
