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Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
OwnerNational Renewable Energy Laboratory (NREL) - view all
Update frequencyunknown
Last updatedabout 1 year 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/5794
ib1_trust_framework[]
language
Files
  • ZIP
    Machine Learning Model Resources.zip
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