A Bayesian Learning Approach for Shoreline Protection Strategies

Daniel Sierra

| June 7th 2022 – 11 am, NYUAD C1 (ERB) -045 |

Coastal cities have historically suffered inundations and Sea-Level Rise is set to worsen it in the coming decades, threatening lives and damaging infrastructure. As cities devise protection plans, some of them have different locations to protect. In order to protect the transportation infrastructure, detailed hydrodynamic and traffic simulations need to be performed. Protection combinations grow exponentially when the number of locations to protect increases, making it infeasible to perform all simulations. Therefore, we propose a Bayesian algorithm that allows to update the set of belief on each location and determine the best locations to protect within a certain budget.

Speaker’s bio

Daniel is an engineer-minded scientist, enrolled in the PhD program of Transportation Engineering at New York University, in its Tandon School of Engineering, performing research at NYU Abu Dhabi. His research focuses on the impact of Sea-Level Rise on transportation systems on coastal cities. He has civil engineering experience in Latin-America, Europe and the Middle East where he worked in transportation infrastructure projects and has a MSc. in Sustainable Critical Infrastructure, with a master’s thesis on the travel behavior of people in the Emirate of Abu Dhabi. He is now pursuing a PhD in Transportation Engineering at NYU.