In this dissertation we consider strategic sensor placement in water distribution systems to detect contaminants in real time, and thus to protect the health and life of the population. Specifically, we apply a set of decision approaches for selecting sensors and locating sensors in water distribution systems, which can be used to help water utilities develop response protocols to and mitigate the impact from contamination events.
We first apply a scenario-based robust approach, which incorporates uncertainties inherent in the problem (random contamination events and dynamic water flow), to design sensor-placement schemes to hedge against the worst consequences across all the realizable contamination scenarios. Single- and multi-objective robust decision models are developed: a robust covering model to minimize the maximum number of nodes uncovered by the sensor network across all contamination scenarios; a robust p-median-based model to minimize the maximum volume of contaminated water consumed prior to detection across all contamination scenarios; and a robust multi-objective model to minimize the maximum value of the weighted objectives (the combination of the above two objectives) across all contamination scenarios. A heuristic solution method is applied to solve the robust decision models. The concept of "sensitivity region", which depicts the range of the possible values of each objective across all contamination scenarios, corresponding to a robust solution for the multi-objective model, is used to generate trade-off relationships between the multiple objectives. This robust approach requires that we know contaminant behavior in a water distribution system and is computationally-costly, especially for medium and large-sized water distribution networks.
We then apply a graph-theoretic approach to identify key nodes in a water distribution network for placing sensors to address the situation when water utilities either can not afford a computationally-costly simulation-based approach (e.g., our scenario-based robust approach) or only have information on the topology of their systems. Two network measures, betweenness centrality and receiveability, are used to identify key sets of nodes for placing sensors, together with the technology for community detection and greedy heuristic algorithms. Betweenness centrality defines the centrality of a node in terms of the degree to which the node falls on the shortest path between other pairs of nodes. In a water distribution network, a node with high betweenness centrality would be between many potential upstream contamination events and downstream receptor populations. Receiveability is used to describe the set and number of nodes that have paths to the measured node in a graph. Receiveability measure the capability of a node with a sensor to detect contamination events. Nodes with high betweenness centrality and/or high receiveability are ideal sensor location nodes.
Finally, we apply a Bayesian Network to infer the system performance, which refers to the probability of detecting a contamination event and the probability of setting off false alarms by the sensor network, based on the number of sensors available, the false positive and false negative rates of individual sensor, and the sensor-placement schemes designed. A "sensor" here is a micro-analytical system which can provide Yes or No to the presence of a contaminant with certain false positive and false negative rates. A maximum covering model is used to design sensor-placement schemes for a given number of sensors to maximize the "area" (the number of nodes) of the water distribution system being covered by a sensor network. The "area" covered by each of the sensors can be identified by tracking the transport and fate of contaminants using a simulation method. The information is then integrated into a Bayesian Network to construct the trade-offs relationship between the number of sensors and the performance of individual sensor (false negative and false positive rates) and thus provide decision support to water utilities.
|School:||Carnegie Mellon University|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-B 68/09, Dissertation Abstracts International|
|Keywords:||Contaminant detection, Environmental decision-making, Real-time detection, Water supply networks|
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