A machine learning approach for mapping the very shallow theoretical geothermal potential
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2195-9706
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
A machine learning approach for mapping the very shallow theoretical geothermal potential ; volume:7 ; number:1 ; day:25 ; month:7 ; year:2019 ; pages:1-50 ; date:12.2019
Geothermal Energy ; 7, Heft 1 (25.7.2019), 1-50, 12.2019
- Klassifikation
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Natürliche Ressourcen, Energie und Umwelt
- Urheber
-
Assouline, Dan
Mohajeri, Nahid
Gudmundsson, Agust
Scartezzini, Jean-Louis
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s40517-019-0135-6
- URN
-
urn:nbn:de:101:1-2019121804245105787552
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:47 MESZ
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Beteiligte
- Assouline, Dan
- Mohajeri, Nahid
- Gudmundsson, Agust
- Scartezzini, Jean-Louis
- SpringerLink (Online service)
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