NH017
Environmental monitoring and future risk planning through data driven applications and high performance computing.

Session ID#: 78897

Session Description:
The complex and interconnected impacts of disasters highlight the importance of multi-level, interdisciplinary science when planning for risks and impacts of a new climate normal. Coupling machine learning techniques with physical science methodologies gives researchers the ability to understand the complex dynamics of changing Earth systems.  This session highlights methodologies and tools that focus on how big data and the internet of things are being leveraged to observe and mitigate risk in environmental decision making.  Utilizing big data contributes to understanding the adverse consequences of increased frequency and intensity of natural hazards and extreme weather events that result in disasters.  Presentations will focus on the fusion of earth observation and multi-resolution approaches that help improve our ability to efficiently solve cross-scale problems
Index Terms:

4306 Multihazards [NATURAL HAZARDS]
4307 Methods [NATURAL HAZARDS]
4328 Risk [NATURAL HAZARDS]
4337 Remote sensing and disasters [NATURAL HAZARDS]

Primary Convener:  Elena Sava, US Army Corps of Engineers Geospatial Research Laboratory, Alexandria, VA, United States
Conveners:  Kimberley Rain Miner, University of Maine, Orono, ME, United States; USACE-ERDC GRL, Geospatial Research Lab, Alexandria, VA, United States and Walter Beckwith, American Association for the Advancement of Science Washington DC, Washington, United States
Primary Liaison:  Laura Clemente-Harding, Pennsylvania State University Main Campus, University Park, PA, United States; US Army Corps of Engineers Geospatial Research Laboratory, Alexandria, VA, United States
 
Meteorological Causes to Fish Kills in the Danshuei River of Taiwan (500892)
You-Jia Chen and Su-Ting Cheng, National Taiwan University, School of Forestry and Resource Conservation, Taipei, Taiwan
 
Increased Risk to Human Health from Mobilized Trace Metals in Groundwater is Linked to Rising Sea Level, Increased Precipitation, and Human Impoundments. (501653)
George L. Jacobson1, Stephen A Norton2, Shaleen Jain3 and Aria Amirbahman3, (1)University of Maine, School of Biology & Ecology; Climate Change Institute, Orono, ME, United States, (2)University of Maine, School of Earth and Climate Sciences, Orono, ME, United States, (3)University of Maine, Civil and Environmental Engineering, Orono, ME, United States
 
Creation of damage area prediction map according to chemical incidents around chemical industrial complex considering environmental factors (535202)
Myeong-je Kim1, Seong-Woo Jeon1 and Jieun Ryu2, (1)Korea University, Environmental Science & Ecological Engineering, Seoul, South Korea, (2)Korea university, Environmental Science & Ecological Engineering, Seoul, South Korea
 
Forest Cover Indices for Identifying Canopy Cover in Multispectral Imagery (540972)
Sarah J. Becker, US Army Corps of Engineers Washington DC, Washington, DC, United States, Kristofer Lasko, University of Maryland College Park, College Park, MD, United States, Craig S.T. Daughtry, USDA ARS, Hydrology & Remote Sensing Lab, Beltsville, MD, United States, Luisa Izette Feliciano-Cruz, University of Puerto Rico, Mayaguez, PR, United States and Andrew L. Russ, USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, United States
 
Deep Reinforcement Learning for Disaster Response: Navigating the Dynamic Emergency Vehicle and Rescue Team Dispatch during a Flood (599596)
Yi-Lin Tsai1, Abhijeet Phatak2, Peter K Kitanidis1 and Christopher B Field3, (1)Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States, (2)Stanford University, Department of Electrical Engineering, Stanford, CA, United States, (3)Stanford University, Stanford Woods Institute for the Environment, Stanford, CA, United States
 
Tornado Damage Detection Utilizing Unpiloted Autonomous Systems, Structure from Motion, and Deep Neural Networks (604379)
Zhiang Chen1, Melissa Wagner2, Robert K Doe3 and Jnaneshwar Das1, (1)Arizona State University, School of Earth and Space Exploration, Tempe, AZ, United States, (2)Arizona State University, School of Geographical Science and Urban Planning, Tempe, AZ, United States, (3)University of Liverpool, Department of Geography and Planning, Liverpool, United Kingdom
 
IoT Sensors and Their Pathway to HPC (622913)
Daniel R Fuka1, Martha E Apple2, James H R Gallagher3, David W Fulker3, Nathan Potter3, Ruth Duerr4, Moges Berbero Wagena1, Eric Lingerfelt5, Michael D Daniels6, Agbeli Ameko7, Scott Dale Peckham8, Kodi Neumiller3, Amy Collick9, Emily M Bock10, Robin R White11, D. Sarah Stamps12 and Zach M Easton1, (1)Virginia Tech, Blacksburg, VA, United States, (2)Montana Technological University, Biological Sciences, Butte, MT, United States, (3)OPeNDAP, Inc., Butte, MT, United States, (4)Ronin Institute for Independent Scholarship, Westminster, CO, United States, (5)Oak Ridge National Laboratory, National Center for Computational Sciences, Oak Ridge, TN, United States, (6)University Corporation for Atmospheric Research, Boulder, United States, (7)National Center for Atmospheric Research, GLOBE, Boulder, United States, (8)University of Colorado, Boulder, CO, United States, (9)University of Maryland Eastern Shore, Princess Anne, MD, United States, (10)Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, (11)Virginia Polytechnic Institute and State University, APSC, Blacksburg, United States, (12)Virginia Polytechnic Institute and State University, Geosciences, Blacksburg, VA, United States
 
Optimizing Long-term Monitoring of Radiation Air Dose Rates (623021)
Dajie Sun, Berkeley, CA, United States
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