LSU Professor Uses AI/ML To Predict Coastal Flooding
Retrieved on:
Tuesday, January 18, 2022
Knowledge, Computer science, Research, Computation, LSU School of Music, Interoperability, University of Notre Dame, Digital twin, DT, BATON, Lexikon verfolgter Musiker und Musikerinnen der NS-Zeit, Electricity, TI Advanced Scientific Computer, Population, Findability, Learning, Flood, GLOBE, Department, Knowledge extraction, FAIR, Wind, Intelligence, ESM, Program, Lawrence Berkeley National Laboratory, Data collection, DOE, Technology, Lai-yung Ruby Leung, Workflow, University, Machine learning, Medical device, Science
These coastal communities and infrastructure are especially vulnerable to wind and flooding due to tropical storms, hurricanes, and heavy rainfall.
Key Points:
- These coastal communities and infrastructure are especially vulnerable to wind and flooding due to tropical storms, hurricanes, and heavy rainfall.
- With the aid of artificial intelligence and machine learning (AI/ML) techniques, LSU School of Computer Science & Electrical Engineering Adjunct Professor Hartmut Kaiser is working to improve their flood preparedness and mitigation capabilities.
- The well-being of all Americans depends on the environmental integrity and sustainable productivity of the ocean, our coasts and coastal watersheds, Kaiser said.
- The current coastal flooding predictive capability is limited by the inadequate representation of coastal processes in the ESM, especially as they relate to coastal hazards, and by a lack of automated workflow for facilitating the two-way information transfer between experimentalists/domain experts and computational scientists.