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Advances in Multi-Criteria Decision Analysis and Multi-Objective Optimization for Sustainable Water Resources and Sediment Management


This dissertation makes new advances in multi-criteria decision analysis (MCDA) and MCDA-based multi-objective optimization (MOO) and applies these methods to new areas of sustainable sediment and water resources management. Chapter 1 briefly introduces some central themes of this dissertation. Chapter 2 presents a new method for identifying preference weights in MCDA decision models. Chapter 3 applies existing tools for MCDA to the new topic area of sustainable marine sand resource use. Chapter 4 presents a new version of the Dredged Material Management Decisions (D2M2) software and applies it to optimize dredged sediment placement for multiple objectives.

In the new approach for preference weight identification presented in Chapter 2, a stakeholder or decision maker is observed playing a game (e.g., a serious video game) with a similar context to a real-world decision problem of interest. As the player make choices within the game, a record is kept of each chosen and non-chosen alternative and its performance data. After gameplay is finished, analysis is performed on the choice data from the gameplay log. Two approaches are demonstrated to best fit weight sets to the observed decisions. A brute force, enumeration approach evaluates all possible weight sets in a discretized weight space and an evolutionary optimization approach, with parameters tuned for a more explorative search, generates, evaluates, and evolves random weight sets within the continuous weight space. In an illustrative case study with a simple water management game, both approaches produce similar results showing a weight space of best fit. Tradeoffs between shorter and longer gameplay and analysis time affect the accuracy and completeness of the results. While further work is needed to validate the decision models inferred from gameplay against the decision models used in real life, this approach has promise for avoiding some cognitive biases and increasing the scalability of weight identification in MCDA applications.

Chapter 3 applies MCDA to sustainably manage sand deposits (borrow areas) on the ocean floor that are dredged for fill material for coastal engineering projects such as beach nourishment. Borrow area users and managers have expressed concern that existing approaches are not sufficiently sustainable, e.g., do not adequately promote the long-term viability of borrow areas and balance environmental, social, and economic concerns. To remedy this, an MCDA workshop was held with stakeholders and subject matter experts from state and federal government, industry, and academia. Workshop participants were asked to develop an MCDA criteria hierarchy for evaluating the sustainable use of marine sand borrow areas, suggest metrics and scoring considerations for those criteria, list best management practices for sustainable borrow area use, and provide additional observations about existing challenges and future recommendations. Each of these products fills a gap in the literature for marine sand resource use.

The D2M2 software advanced and applied in Chapter 4 creates MCDA-based MOO models of dredged material placement scenarios. This new version incorporates several features to better specify costs, benefits, and impacts and to support the modeler in developing useful solutions. It is applied in a case study using realistic site and management data for dredging and sediment placement along the Gulf Intracoastal Waterway (GIWW) near Galveston, TX. The site data are optimized in nine scenarios that vary the site network and weighting scheme for seven objectives that include financial cost, environmental impacts, and beneficial uses and effects. Results show tradeoffs between impacts and benefits, identify proposed sites most likely to be useful for system management, and highlight the need for additional placement capacity in the system over the 20-year timeframe, a need that can largely be filled through the creation of proposed beneficial use sites included in the model.

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