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Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods

Qin, Tian
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385

Abstract Details

2018, PhD, University of Cincinnati, Engineering and Applied Science: Environmental Engineering.
Water demand estimation is important for representing the underlying hydraulics in the water distribution system that drives water quality dynamics. With respect to demand estimation, clustering water-use nodes within network models reduces the number of unknowns, improves the efficiency of algorithm and is needed to produce a feasible estimation problem. The objectives of this research are to propose a clustering algorithm to reduce parameterization for demand estimation, develop a Markov chain Monte Carlo demand estimation algorithm incorporating spatial correlation in demands and generating uncertainty estimates, and implement a real-world large-scale water distribution system case study to investigate complexity of demand estimation problems. The identification of monitoring or sensor locations within water distribution systems can be challenging given the size of realistic network. Approaches such as skeletonization or aggregation can effectively reduce the size of network models but are generally more appropriate for satisfying hydraulic objectives. The proposed approach uses an input-output relationship to assess hydraulic path between any two nodes, which serves as a surrogate for water quality dynamics. For two different case studies, as number of clusters increased, the nodes within each cluster became more similar. The resulting clusters provide opportunities, for example, to reduce the problem size for monitoring or sensor selection. The use of water distribution system models has been around for decades and requires good demand estimates to ensure adequate hydraulic and water quality representation. Traditional optimization approaches are often used to estimate demands, generally for highly skeletonized systems, with approximations to represent the uncertainties in demand estimates and hydraulic states. The proposed Markov chain Monte Carlo (MCMC) algorithm is capable of estimating both the expected values and uncertainties of demands estimates. The MCMC approach also provides flexibility to accommodate potential spatial correlation in water demands through use of a Markov Random Field (MRF) prior. The MCMC algorithm produced adequate representation of water demands, similar to weighted least squares, but did not rely on approximations to represent uncertainties. The incorporation of the MRF prior resulted in more spatially correlated demands, but provided no significant benefits for the network being studied. Increasing the number of clusters, reducing measurement uncertainty and including additional flow measurements improved the ability to represent the system-wide flows. The application of water demand estimation to real-world systems is challenging as the number of hydraulics meters (flow rates and pressures) is limited. To estimate water demands for an actual network, two clustering methods were applied – one based on hydraulic path similarity and one based on improving observability for demand estimation problem. Both clustering approaches improved representation of the tracer signals at the majority of locations, but the observability-based clustering approach provided improved representation along the main trunk lines and slightly more of the tracer monitoring locations. The application to the real-world system identified some significant challenges associated with demand estimation problem in relation to representing underlying hydraulics for transport, and has been an important first effort improving the approach for demands estimation and water quality representation.
Dominic Boccelli, Ph.D. (Committee Chair)
Sivaraman Balachandran, Ph.D. (Committee Member)
Michael Eugene Tryby, Ph.D. (Committee Member)
James Uber, Ph.D. (Committee Member)
159 p.
Environmental Engineering
water demands estimation; MCMC algorithm; Clustering; large water network; tracer study

Recommended Citations

Citations

  • Qin, T. (2018). Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385

    APA Style (7th edition)

  • Qin, Tian. Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods. 2018. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385.

    MLA Style (8th edition)

  • Qin, Tian. "Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods." Doctoral dissertation, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385

    Chicago Manual of Style (17th edition)

ucin1544002222852385
125
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This open access ETD is published by University of Cincinnati and OhioLINK.