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Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
L o a d i n g
Organization
National Renewable Energy Laboratory (NREL) - view all
Update frequencyunknown
Last updated4 days ago
Format
Overview

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.

ANNAlgorithmBNNBayesianELMGreat BasinMachine LearningNMFNeural NetworkNevadaPCAPFAPlay FairwayPrincipal Componentcharacterizationenergyexplorationfeature setgeothermalgeotiffinputsoutputsrastertraining sites
Additional Information
KeyValue
Dcat Issued2021-06-01T06:00:00Z
Dcat Modified2022-11-07T07:37:39Z
Dcat Publisher NameNevada Bureau of Mines and Geology
Guidhttps://data.openei.org/submissions/7465
Harvest Object Id505a5489-8dc4-48c8-bde9-67e12794e4f3
Harvest Source Id4eb7107f-a2b1-40e3-b36a-8161aa98a56e
Harvest Source TitleOpenEI Data Portal
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Trust Signals
Trust Framework(s)None
Assuranceunknown
Data Sensitivity Classunknown
Licenceunknown
Files
  • Machine Learning Model Resources.zip