Predicting Geothermal Favorability in the...
URL: https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=35430
This study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment.
Additional Information
| Field | Value |
|---|---|
| Data last updated | January 11, 2025 |
| Metadata last updated | September 23, 2025 |
| Created | January 11, 2025 |
| Format | HTML |
| License | Creative Commons Attribution |
| Datastore active | False |
| Has views | True |
| Id | f76f2f18-b69f-4686-9968-01d40034497e |
| Mimetype | text/html |
| Package id | 0d122e9c-a5cb-4dc4-8b59-fc57c1051f23 |
| Position | 3 |
| State | active |