Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.
The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.
