An Evaluation of AI Models' Performance for Three Geothermal Sites

dc.authorid duzgun, sebnem/0000-0001-7013-9241
dc.authorid Demir, Ebubekir/0000-0002-1680-3719
dc.authorscopusid 57195223052
dc.authorscopusid 56705577200
dc.authorscopusid 57203966269
dc.authorscopusid 6508150239
dc.authorwosid duzgun, sebnem/F-4244-2017
dc.contributor.author Çavur, Mahmut
dc.contributor.author Cavur, Mahmut
dc.contributor.author Yu, Yu-Ting
dc.contributor.author Duzgun, H. Sebnem
dc.contributor.other Management Information Systems
dc.date.accessioned 2024-10-15T19:41:07Z
dc.date.available 2024-10-15T19:41:07Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp [Demir, Ebubekir; Cavur, Mahmut; Yu, Yu-Ting; Duzgun, H. Sebnem] Colorado Sch Mines, Min Engn Dept, Golden, CO 80401 USA; [Cavur, Mahmut] Kadir Has Univ, Management Informat Syst Dept, TR-34083 Istanbul, Turkiye en_US
dc.description duzgun, sebnem/0000-0001-7013-9241; Demir, Ebubekir/0000-0002-1680-3719 en_US
dc.description.abstract Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites-Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals. en_US
dc.description.sponsorship US Department of Energy [DE-EE0008760] en_US
dc.description.sponsorship This research was funded by the US Department of Energy grant number DE-EE0008760, and the APC was funded by DE-EE0008760. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.3390/en17133255
dc.identifier.issn 1996-1073
dc.identifier.issue 13 en_US
dc.identifier.scopus 2-s2.0-85198524168
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.3390/en17133255
dc.identifier.uri https://hdl.handle.net/20.500.12469/6409
dc.identifier.volume 17 en_US
dc.identifier.wos WOS:001269885400001
dc.identifier.wosquality Q3
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 2
dc.subject geothermal AI en_US
dc.subject geothermal delineation en_US
dc.subject modified geothermal AI en_US
dc.subject renewable energy en_US
dc.subject sustainable energy en_US
dc.subject geothermal resource assessment en_US
dc.title An Evaluation of AI Models' Performance for Three Geothermal Sites en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication
relation.isAuthorOfPublication 463fefd7-0e68-4479-ad37-0ea65fa6ae01
relation.isAuthorOfPublication.latestForDiscovery 463fefd7-0e68-4479-ad37-0ea65fa6ae01
relation.isOrgUnitOfPublication ff62e329-217b-4857-88f0-1dae00646b8c
relation.isOrgUnitOfPublication.latestForDiscovery ff62e329-217b-4857-88f0-1dae00646b8c

Files