Speaker
Description
Developing a general-purpose AI model for infrastructure resiliency assessments requires integration across a variety disciplines sectors and data heterogeneity and inference for a range of needs. When we talk about resilience to hydrological and meteorological hazards, hazard forecasting at time scales extending from few minutes to 100’s of years is necessary for addressing resiliency of grid / infrastructure for the immediate threat to develop and implement plans for low frequency and high impact events. These forecasts also need to be at a scale that is relevant to the infrastructure. We are making progress in building large Foundation model for medium range weather forecasting (1-10 days) with increasingly higher spatial resolution. Extending these models to seasonal-to-sub seasonal, interannual, decadal and climate scales is a challenge that is being addressed now. Integrating these hazard models with infrastructure resiliency models (i.e. fragility curves), socioeconomic models to estimate vulnerability of the infrastructure and exposure of the population to impacts for estimating risk and developing strategies for mitigating that risk is the goal of resiliency models. Initial attempts at integrating AI models developed across sectors is an active area of research. One such model our group is working is building a coupling between AI weather models and individual based decision models at Urban scale. The possibilities for integrating infrastructure models and scaling these types of models to regional and national scale is an emerging opportunity.
