IN016:
Computationally-Intelligent Solutions for Resource Questions in Earth Science





Session ID#: 24874

Session Description:
Our ability to explore, assess, monitor, and manage earth resources is being transformed by a range of new technologies including big data (variety, velocity, volume) sensors, computationally-intelligent workflows, and supercomputing infrastructure. This session aims to bring together researchers who integrate big data sets (e.g., direct: biological, chemical, and physical; and indirect: in situ, ground, airborne, and satellite sensors) using novel computationally-intelligent modeling workflows (e.g., combining data science with predictive analytics, numerical, and/or statistical methods). Topics of particular interest (but not limited to) include challenges and solutions to characterize, predict, and quantify processes and responses across spatiotemporal scales among sparse and uncertain earth science data (e.g., agriculture, climate, ecology, energy, geology, geophysics, hydrogeology, hydrology, minerals, natural hazards, remote sensing, solid earth, soils, sociology, and space).
Primary Convener:  Michael J Friedel, GNS Science, Lower Hutt, New Zealand; University of Colorado Denver, Mathematical & Statistical Sciences, Denver, CO, United States
Conveners:  Ken Lawrie, Geoscience Australia, Canberra, Australia and Massimo Buscema, Semeion Institute, Rome, Italy; University of Colorado Denver, Mathematical and Statistical Sciences, Denver, CO, United States
Co-Organized with:
Earth and Space Science Informatics, and Hydrology

Cross-Listed:
  • GC - Global Environmental Change
  • H - Hydrology
  • NH - Natural Hazards
  • NS - Near Surface Geophysics
Index Terms:

0933 Remote sensing [EXPLORATION GEOPHYSICS]
1835 Hydrogeophysics [HYDROLOGY]
1942 Machine learning [INFORMATICS]
4315 Monitoring, forecasting, prediction [NATURAL HAZARDS]

Abstracts Submitted to this Session:

Jennifer Seiter, US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, United States, John Furey, US Army Engineer Research and Development Center, Vicksburg, MS, United States and Austin Davis, US Army Engineer Research and Development Center Vicksburg, Vicksburg, MS, United States
Bin Hu and Bo Wan, China University of Geosciences, Wuhan, China
Nicola Ferralis1, Jeffrey Grossman1 and Roger E Summons2, (1)Massachusetts Institute of Technology, Materials Science and Engineering, Cambridge, MA, United States, (2)MIT Lincoln Laboratory, Lexington, MA, United States
Matthew J Cracknell1, Laura Jackson2, Anita Parbhakar-Fox2 and Katerina Savinova3, (1)University of Tasmania, ARC Research Hub for Transforming the Mining Value Chain, Hobart, TAS, Australia, (2)University of Tasmania, ARC Research Hub for Transforming the Mining Value Chain, Hobart, Australia, (3)Corescan Pty Ltd, Perth, Australia
Amie Elizabeth Corbin1, Joris Timmermans1,2, Leon Hauser1, Peter van Bodegom1 and Nadia A Soudzilovskaia1, (1)Leiden University, Institute of Environmental Sciences (CML), Leiden, Netherlands, (2)University College London, London, United Kingdom