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Multifaceted information fusion : a decision support system for online monitoring in water distribution networks

University of British Columbia

Online monitoring of distributed infrastructure systems such as water distribution networks is necessary to assure the safety and security of our modern societies. Often various patterns of uncertainty and information deficiency about spatial relationships and interdependence of distributed observations impede effective monitoring of distributed systems. As a result, spatiotemporal monitoring of complex infrastructure systems needs a unified framework to deal with various sources of information deficiency induced by subjective and objective information. This thesis investigates the problem of combining deficient spatiotemporal evidential sources for the purpose of quality monitoring and relative risk analysis. Spatiotemporal monitoring of water distribution networks based on surrogate water quality parameters is formulated in terms of integrated quality monitoring and relative risk analysis. Distributed quality assessment is performed through an extended fuzzy evidential reasoning scheme that employs the fuzzy interval grade and interval-valued belief degree (IGIB). The former facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge whereas the latter allows for handling the lack of information on belief degree assignments. Local multi-sensor fusion for relative risk analysis is based on a proposed extended fuzzy belief rule-based (BRB) system that employs the IGIB structure in its rule consequent. The proposed extended BRB system can handle nonlinear input-output relationships and inconsistencies in the inference process as well as epistemic uncertainties including ambiguity, vagueness, and interval uncertainty in the knowledge base. A multi-level information fusion framework capable of modeling interdependency and uncertainty is proposed for distributed online monitoring. The multi-level information fusion framework encompasses a networked fuzzy belief rule based (NF-BRB) system and a high-level BRB system. The NF-BRB system is employed for knowledge elicitation from the expert about locally perceived relative risk associated with drinking water, while the high-level BRB system with hybrid learning enhances the event detection performance. Extensive simulated water quality degradation events, originated from real dataset of the Quebec City’s main water distribution network and contamination tests in a pilot distribution facility, were exploited to validate the efficacy of the proposed framework for integrated water quality monitoring and relative risk analysis.

Text

2014-02-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/45991

Mechanical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/