IN11E:
Predictive Analytics in the Earth and Space Sciences Posters


Submit an Abstract to this Session


Session ID#: 53348

Session Description:
Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not only concerned with individual analyses or analysis steps, but with an entire methodology.

Predictive Analytics is a branch of data analytics that uses statistics, data mining, machine learning, modeling and artificial intelligence techniques to make assertions about the future or about currently unknown information.

This session welcomes submission on any application of analytics which may be used to predict events, relationships, assertions, or any currently unknown information.

Primary Convener:  Anirudh Prabhu, Carnegie Institution for Science, Earth and Planets Laboratory, Washington, United States
Conveners:  Fang Huang, Rensselaer Polytechnic Institute, Tetherless World Constellation, Troy, NY, United States; CSIRO, Mineral Resources, Kensington, WA, Australia, Xiaogang Ma, University of Idaho, Department of Computer Science, Moscow, ID, United States and Shaunna M Morrison, Carnegie Institution for Science Washington, Geophysical Laboratory, Washington, DC, United States
Primary Liaison:  Anirudh Prabhu, Carnegie Institution for Science, Earth and Planets Laboratory, Washington, United States
Chairs:  Anirudh Prabhu, Carnegie Institution for Science, Earth and Planets Laboratory, Washington, United States and Fang Huang, CSIRO, Mineral Resources, Kensington, WA, Australia
OSPA Liaison:  Shaunna M Morrison, Carnegie Institution for Science, Earth and Planets Laboratory, Washington, United States
Index Terms:

1942 Machine learning [INFORMATICS]
1952 Modeling [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
3238 Prediction [MATHEMATICAL GEOPHYSICS]

Abstracts Submitted to this Session:

Predictive Analytics for Earth and Space Sciences: From ET to Earthquakes (348989)
Harold A Geller, George Mason University Fairfax, Physics and Astronomy, Fairfax, VA, United States
Unmixing mineral phases, connecting measurements: Using machine learning to understand natural process heterogeneity (Invited) (352813)
Joshua Franz Einsle1, Ben Martineau2, Iris Buisman3, Giulio Isacco Lampronti4, Zoja Vukmanovic5, Duncan Johnstone2, Alexander Eggeman6, Paul A Midgley7 and Richard J Harrison3, (1)University of Cambridge, Department of Materials Science and Metallurgy, Department of Earth Sciences, Cambridge, United Kingdom, (2)University of Cambridge, Materials Science and Metallurgy, Cambridge, United Kingdom, (3)University of Cambridge, Department of Earth Sciences, Cambridge, United Kingdom, (4)University of Cambridge, Earth Sciences, Cambridge, United Kingdom, (5)Rickmansworth, United Kingdom, (6)University of Manchester, Materials, Manchester, United Kingdom, (7)University of Cambridge, Cambridge, United Kingdom
Predictive Analytics in Earth Science Communication (Invited) (363111)
Tom Narock, Notre Dame of Maryland University, Baltimore, MD, United States
Predicting New Mixture Spectra with a Spectral Mixture Model derived using Principle Component Analysis (422669)
Joseph S Makarewicz and Heather D Makarewicz, Olivet Nazarene University, Bourbonnais, IL, United States
Accelerating decision making in greenfields minerals exploration field campaigns (424206)
Jess Robertson1, David Cole2, Alistair White3, Francky Fouedjio4,5, Ryan R.P. Noble6, Nathan Reid4, Maciej Golebiewski7, Jens F Klump8 and Ryan Fraser9, (1)CSIRO Mineral Resources Flagship, Perth, WA, Australia, (2)CSIRO Data61, Melbourne, VIC, Australia, (3)CSIRO Mineral Resources Flagship, Perth, Australia, (4)CSIRO Mineral Resources, Perth, WA, Australia, (5)Stanford University, Department of Geological Sciences, Stanford, WA, United States, (6)CSIRO Mineral Resources, Kensington, WA, Australia, (7)CSIRO IM&T, Melbourne, VIC, Australia, (8)CSIRO Mineral Resources, Perth, Australia, (9)CSIRO, Kensington, WA, Australia
Using a Knowledge Graph to Discover Earth Science Information (439411)
Brian Freitag1, Rahul Ramachandran2, J.J. Miller3, Kaylin Bugbee4, Jia Zhang5, Xiaoyi Duan5, Manil Maskey2, Patrick N Gatlin4 and Sundar A Christopher6, (1)University of Alabama in Huntsville, Huntsville, United States, (2)NASA Marshall Space Flight Center, Huntsville, United States, (3)University of Alabama in Huntsville, ESSC, Huntsville, AL, United States, (4)NASA Marshall Space Flight Center, Huntsville, AL, United States, (5)Carnegie Mellon University Silicon Valley, Moffett Field, CA, United States, (6)University of Alabama in Huntsville, Atmospheric and Earth Science, Huntsville, AL, United States
Application of Meta-Heuristic Probabilistic Genetic Algorithms and Machine Learning Surrogate Models to Recognize Optimal Patterns of Green Stormwater Infrastructure Networks and Predict their Performance in Urban Watersheds (442392)
Bardia Heidari Haratmeh, University of Illinois at Urbana Champaign, Urbana, IL, United States; Southern Methodist University, Civil and Environmental Engineering, Dallas, TX, United States and Barbara E Minsker, Univ Illinois Urbana-Champaign, Dallas, United States
Clarifying likelihood of subsurface fluid/gas migration using fuzzy logic (467844)
Jennifer R Bauer1, Devin Justman2, Katherine Jones3, Patrick Wingo2, Jennifer DiGiulio4, Christopher Gabriel Creason5 and Kelly K Rose1, (1)National Energy Technology Laboratory, Albany, OR, United States, (2)Leidos Research Support Team, Albany, OR, United States, (3)Oak Ridge Institute for Science and Education, Albany, OR, United States, (4)AECOM, Albany, OR, United States, (5)National Energy Technology Laboratory, Albany, United States

See more of: Earth and Space Science Informatics