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Title: Planning water resource systems under uncertainty
Author: Matrosov, E. S.
ISNI:       0000 0004 5367 1879
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Stationarity assumptions of linked human-water systems are frequently invalid given the difficult-to-predict changes affecting such systems. Population growth and development is fuelling rising water demand whilst in some parts of the world water supply is likely to decrease as a result of a changing climate. A combination of infrastructure expansion and demand management will be necessary to maintain the water supply/demand balance. The inherent uncertainty of future conditions is problematic when choosing a strategy to upgrade system capacity. Additionally, changing stakeholder priorities mean multi-criteria planning methods are increasingly relevant. Various modelling-assisted approaches are available to help the water supply planning process. This thesis investigates three state-of-the-art multi-criteria water source systems planning approaches. The first two approaches seek robust rather than optimal solutions; they both use scenario simulation to test the system plans under different plausible versions of the future. Under Robust Decision Making (RDM) alternative strategies are simulated under a wide range of plausible future scenarios and regret analysis is used to select an initial preferred strategy. Statistical cluster analysis identifies causes of system failure enabling further plan improvement. Info-Gap Decision Theory tests the proposed strategies under plausible conditions that progressively deviate from the expected future scenario. Decision makers then use robustness plots to determine how much uncertain parameters can deviate from their expected value before the strategies fail. The third approach links a water resource management simulator and a many-objective evolutionary search algorithm to reveal key trade-offs between performance objectives. The analysis shows that many-objective evolutionary optimisation coupled with state-of-the art visual analytics helps planners assess the best (approximately Pareto-optimal) plans and their inherent trade-offs. The alternative plans are evaluated using performance measures that minimise costs and energy use whilst maximising engineering and environmental performance criteria subject to basic supply reliability constraints set by regulators. The analyses show that RDM and Info-Gap are computationally burdensome but are able to consider a small number of candidate solutions in detail uncovering the solutions’ vulnerabilities in the face of uncertainty in future conditions while the multi-objective optimisation approach is able to consider many more possible portfolios and allow decision makers to visualize the trade-offs between performance metrics.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available