Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation
Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), ne...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-85050-3 |
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| author | Jonathan E. Gordon Olatunde D. Akanbi Deepa C. Bhuvanagiri Hope E. Omodolor Vibha Mandayam Roger H. French Jeffrey M. Yarus Erika I. Barcelos |
| author_facet | Jonathan E. Gordon Olatunde D. Akanbi Deepa C. Bhuvanagiri Hope E. Omodolor Vibha Mandayam Roger H. French Jeffrey M. Yarus Erika I. Barcelos |
| author_sort | Jonathan E. Gordon |
| collection | DOAJ |
| description | Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment. |
| format | Article |
| id | doaj-art-72db15415fc6486f85be75e7b4e68bf6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-72db15415fc6486f85be75e7b4e68bf62025-01-12T12:15:13ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-85050-3Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradationJonathan E. Gordon0Olatunde D. Akanbi1Deepa C. Bhuvanagiri2Hope E. Omodolor3Vibha Mandayam4Roger H. French5Jeffrey M. Yarus6Erika I. Barcelos7Materials Data Science for Stockpile Stewardship: Center of Excellence, Case Western Reserve UniversityDepartment of Materials Science, Case Western Reserve UniversityDepartment of Computer and Data Sciences, Case Western Reserve UniversityDepartment of Earth and Planetary Sciences, Case Western Reserve UniversityDepartment of Computer and Data Sciences, Case Western Reserve UniversityDepartment of Materials Science, Case Western Reserve UniversityDepartment of Materials Science, Case Western Reserve UniversityDepartment of Materials Science, Case Western Reserve UniversityAbstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.https://doi.org/10.1038/s41598-024-85050-3Subsurface temperatureTemperature interpolationGeothermal gradientKrigingLightGBMMachine learning |
| spellingShingle | Jonathan E. Gordon Olatunde D. Akanbi Deepa C. Bhuvanagiri Hope E. Omodolor Vibha Mandayam Roger H. French Jeffrey M. Yarus Erika I. Barcelos Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation Scientific Reports Subsurface temperature Temperature interpolation Geothermal gradient Kriging LightGBM Machine learning |
| title | Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation |
| title_full | Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation |
| title_fullStr | Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation |
| title_full_unstemmed | Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation |
| title_short | Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation |
| title_sort | geospatial modeling of near subsurface temperatures of the contiguous united states for assessment of materials degradation |
| topic | Subsurface temperature Temperature interpolation Geothermal gradient Kriging LightGBM Machine learning |
| url | https://doi.org/10.1038/s41598-024-85050-3 |
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