NH006
Big Data for Big Disasters: The solutions and shortcomings of relying on technology to solve global disaster problems
Session ID#: 81595
Session Description:
Big data, AI, and technology are often heralded as the solution to myriad problems. In the context of natural disasters, what are the applications and limitations of these resources? Many earth scientists think of big data in the context of modeling or prediction, but there are numerous other innovative applications being developed in all corners of the world. This panel discussion will draw together experts from disaster research and the tech sector to discuss the ways in which technologies around the world are being applied to solve disaster problems, lessons learned from failed experiments, the ways in which tech can't help, and the future role of technology for natural disaster resilience.
Index Terms:
1930 Data and information governance [INFORMATICS]
4315 Monitoring, forecasting, prediction [NATURAL HAZARDS]
4316 Physical modeling [NATURAL HAZARDS]
4339 Disaster mitigation [NATURAL HAZARDS]
Primary Convener: Jacqueline Ratner, Columbia University Earth Institute, National Center for Disaster Preparedness, New York, NY, United States
Conveners: Diana Zamora-Reyes, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, Paula R Buchanan, Jacksonville State University, Department of Emergency Management, Jacksonville, AL, United States and Kimberly Wood, Mississippi State University, Department of Geosciences, Mississippi State, MS, United States
Primary Liaison: Jacqueline Ratner, Columbia University Earth Institute, National Center for Disaster Preparedness, New York, NY, United States
New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge? (553557)
Angela Li1, Chung-Lin Shie2, Jennifer C Wei3, Long Pham4 and David J Meyer3, (1)NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States, (2)NASA/GSFC, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)NASA - GSFC, Greenbelt, MD, United States
A Data Driven Approach to Identify Rainfall Derived Inflow and Infiltration in Sanitary Sewer Sub-catchments (555698)
Zhe Zhang1, Tuija Laakso2, Seppo Pulkkinen3, Suvi Ahopelto2, Chi Zhang4, Yu Li4, Ximing Cai5, Zhuping Sheng6, Riku Vahala2 and Kirsi Virrantaus7, (1)Texas A&M University College Station, College Station, TX, United States, (2)Aalto University, Department of Built Environment, Espoo, Finland, (3)Finnish Meteorological Institute, Helsinki, Finland, (4)Dalian University of Technology, School of Hydraulic Engineering, Dalian, China, (5)University of Illinois at Urbana Champaign, Department of Civil and Environmental Engineering, Urbana, IL, United States, (6)Texas A&M AgriLife Research, El Paso, TX, United States, (7)Aalto University School of Engineering, Department of Built Environment, Espoo, Finland
Identifying Wildfire Tweets by Semantic Analysis and Integrating Environmental Data Using Machine Learning (630837)
Yicong Huang1, Yuan Fu1, Tingxuan Gu1, Qiaonan Huang1, Shu Li2, Yutong Wang3, Shiling Zhang1, Yichi Zhang3, Yue Zhuang3, Phu Nguyen2 and Chen Li1, (1)University of California Irvine, Computer Science, Irvine, CA, United States, (2)University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States, (3)University of California Irvine, Irvine, CA, United States
Bringing diverse datasets on societal vulnerability and resilience into assessments of post-disaster recovery needs (633618)
Jamie W. McCaughey1, Sabine Loos2, Ritika Singh3, Nama Budhathoki3 and David Lallemant4, (1)ETH Zurich, Institute for Environmental Decisions, Zurich, Switzerland, (2)Stanford University, Stanford, CA, United States, (3)Kathmandu Living Labs, Kathmandu, Nepal, (4)Earth Observatory of Singapore, Asian School of the Environment, Singapore, Singapore
