AI-supported Monitoring and Resiliency Analysis for the Coastal Area of San Juan, Puerto Rico

Major airports are already at risk of coastal flooding due to sea level rise, intensifying storm surges, and tidal variation. For example, the San Francisco Airport was found at a high risk from storm events and sea level rise (M&N, 2015). The coastal area in Puerto Rico, an overseas US territory that needs resources to recover from Hurricane Irma and Maria and to face future devastating coastal hazards in the economic crisis, has been facing the challenge of coastal flooding, erosion, and storm damage. Field observation is needed to support the potential vulnerability assessment and climate adaptation plan.

The Luis Muñoz Marín International Airport in Puerto Rico is the busiest airport and the biggest transportation hub in the Caribbean region. A numerical study discovered that the airport is located in a vulnerable location and is experiencing frequent floods (Justiniano and Mercado, 2019). But the numerical model has not been fully validated due to the lack of field measurements. In addition, the airport and its shoreline have no reliable monitoring data to support the future climate adaptation plan given the rapid sea-level rise and intensifying storms. We are targeted to develop a coastal hazard monitoring system in the coastal area of the airport to fill the gap. Two Surveillance cameras were installed along the shoreline to monitor the water depth and extent as well as the roads, buildings, and other infrastructure systems. An AI-based data processing scheme based on monoplotting and supervised learning is being developed to assess and map floods and infrastructure damage. The research result will be used to inform the asset owners and managers about the regional vulnerability and recommendations will be given to support the planning of climate adaptation measures.