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<p>Sea level rise is a growing threat to coastal communities across the United States. Uncertainty about the extent of sea level rise poses a challenge for creating infrastructure to address this threat. Due to disagreement among decision makers on the severity of climate change, it can be challenging to determine the appropriate level of preparation for the future, which can lead to potential under or over-preparedness. To combat this uncertainty, the US Department of Defense has embraced a robust decision-making model. Decision makers should incorporate multiple future models of the world into their decision-making process. This thesis describes an effort to address these challenges at the United States Naval Academy using a decision support approach called many-objective robust decision-making. Considering decisions to upgrade their seawall at varying heights and at twenty-year intervals, a genetic algorithm was employed to identify a frontier of non-dominated upgrade strategies. Three strategies from the frontier were evaluated in hundreds of possible scenarios with varied discount rates, sea level rise projections, changes to future storminess, and building replacement costs. An analysis was performed to determine in which future conditions those three strategies were vulnerable to failing to meet objectives of having a benefit cost ratio of over 1 and limiting damage to less than $100 million over an 80-year planning horizon. This analysis will enable the US Naval Academy to determine the effectiveness of their seawall upgrade plans for preventing storm surge damage over a range of future scenarios and stakeholder preferences. Ultimately, this research found that upgrading the Naval Academy’s seawall in the near future is critical to avoiding costly damage from flooding. This research also emphasizes how variations to the assumed discount rate can reshape a cost benefit analysis.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22704859 |
| Date | 26 April 2023 |
| Creators | Robert Pekarek (15361783) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Thesis_Pekarek_pdf/22704859 |
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