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Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

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Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.

Applying Data-Driven Machine Learning to Geothermal Favorability in Western United States

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Geothermal resource favorability - select features and predictions for the western United States

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Predicting Geothermal Favorability in the Western United States by Using Machine Learning - Addressing Challenges and Developing Solutions

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What Matters Most - Measuring Feature Importance for Geothermal Resources Using Supervised Learning

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When Less Is More - How Increasing the Complexity of Machine Learning Strategies for Geothermal Energy Assessments May Not Lead toward Better Estimates

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Citation Formats

United States Geological Survey. (2023). Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments [data set].  Retrieved from https://gdr.openei.org/submissions/1498.

Export Citation to RIS

Mordensky, Stanley P., Lipor, John J., DeAngelo, Jacob, Burns, Erick R., and Lindsey, Cary R. Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments. United States: N.p., 07 Feb, 2023. Web. https://gdr.openei.org/submissions/1498.

Mordensky, Stanley P., Lipor, John J., DeAngelo, Jacob, Burns, Erick R., & Lindsey, Cary R. Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments. United States. https://gdr.openei.org/submissions/1498

Mordensky, Stanley P., Lipor, John J., DeAngelo, Jacob, Burns, Erick R., and Lindsey, Cary R. 2023. "Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments". United States.  https://gdr.openei.org/submissions/1498.

@div{oedi_1498, title = {Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments}, author = {Mordensky, Stanley P., Lipor, John J., DeAngelo, Jacob, Burns, Erick R., and Lindsey, Cary R.}, abstractNote = {Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.



This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.}, doi = {}, url = {https://gdr.openei.org/submissions/1498}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {02}}

Details

Data from Feb 7, 2023

Last updated Jul 25, 2023

Submitted May 1, 2023