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Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites.
See readme .txt files and final report for additional metadata.
A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.
Complete Metadata
| bureauCode |
[ "019:20" ] |
|---|---|
| dataQuality | true |
| DOI | 10.15121/1897036 |
| identifier | https://data.openei.org/submissions/7465 |
| issued | 2021-06-01T06:00:00Z |
| landingPage | https://gdr.openei.org/submissions/1351 |
| programCode |
[ "019:006" ] |
| projectLead | Mike Weathers |
| projectNumber | EE0008762 |
| projectTitle | Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada |
| spatial | {"type":"Polygon","coordinates":[[[-119.73848125,38.393557308820284],[-114.3442953125,38.393557308820284],[-114.3442953125,41.864861026951054],[-119.73848125,41.864861026951054],[-119.73848125,38.393557308820284]]]} |
