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ABSTRACT:
With increased frequency and severity of disasters, mitigation planning in critical infrastructures such as transportation and water systems in a key strategy in improving resilience and reducing the potential threats to society. However, identifying the most cost-effective mitigation investments requires reasoning about complex infrastructure networks and connectivity, as well as limited budget resources. In this talk, Dr. Dilkina will present her AI approaches for two important use cases. First, Dr. Dilkina will demonstrate approaches for maximizing mobility in road networks with respect to flood hazards, and the ability to combine machine learning predictive models with planning optimization. Second, she will present our work in Los Angeles on scalable Mixed-Integer Programming approaches to mitigation planning for water infrastructure resilience to earthquakes.
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