Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
- Nevada Bureau of Mines and Geology
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.
- Research Organization:
- DOE Geothermal Data Repository; Nevada Bureau of Mines and Geology
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
- Contributing Organization:
- Nevada Bureau of Mines and Geology
- DOE Contract Number:
- EE0008762
- OSTI ID:
- 1897036
- Report Number(s):
- 1351
- Availability:
- GDRHelp@ee.doe.gov
- Country of Publication:
- United States
- Language:
- English
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