Co-production with Water Managers to Evaluate Multiobjective Evolutionary Algorithm (MOEA)-assisted Optimization for Long Term Water Utility Planning and Shape Future Research Agendas

Smith, Rebecca M.

Graduate Thesis Or Dissertation

Co-production with Water Managers to Evaluate Multiobjective Evolutionary Algorithm (MOEA)-assisted Optimization for Long Term Water Utility Planning and Shape Future Research Agendas Público Deposited

Analytics

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/79407x45d

Abstract

Many promising tools and methods developed in water resources systems analysis research have seen little uptake outside of academia. This may be due to a lack of effective communication about the research to water managers, or it may be because the tools are not ultimately useful or usable in practice. Current predominant research frameworks do not provide insight into these issues or facilitate the incorporation of industry needs into research agendas. This dissertation introduces a structured research approach called the Participatory Framework for Assessment and Improvement of Tools (ParFAIT) that formally connects researchers and water managers in purposeful, iterative exercises to educate about promising tools, evaluate their usefulness and usability, and draw practitioner feedback into academic agendas. The process is founded on co-production concepts and involves two workshops which are designed to ultimately result in: a broadly relatable vehicle to demonstrate the tool (a testbed), practitioner feedback about the tool resulting from hands-on workshop experience, tool-specific as well as more general industry context, and definitive suggestions for increasing the relevance of future research. ParFAIT is demonstrated by testing Multiobjective Evolutionary Algorithm (MOEA)-assisted optimization for long term water utility planning with a group of Front Range, Colorado, water managers. The first workshop informed the creation of the Eldorado Utility Planning Model, a complex but hypothetical testbed designed to be widely relatable to participants. MOEA-assisted optimization was performed on the testbed using workshop-informed formulations of planning decisions, objectives, constraints, and planning scenarios. The optimization results formed the basis of a second workshop at which managers worked directly with testbed output in structured activities and discussions. This ParFAIT study found that practitioners consider the information provided by MOEA-assisted optimization to be useful for several aspects of their long term planning processes, but that there are important considerations for ensuring usability of the tool itself and its output. One important consideration is the interpretation of complex MOEA results. Based on this feedback, this work presents a novel application of Multivariate Regression Tree analysis to extract system insights from MOEA-assisted optimization results.

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