A machine learning approach for mapping the very shallow theoretical geothermal potential. / Assouline, Dan; Mohajeri, Nahid; Gudmundsson, Agust; Scartezzini, Jean-Louis.

In: Geothermal Energy, Vol. 7, 19, 25.07.2019, p. 1-50.

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A machine learning approach for mapping the very shallow theoretical geothermal potential. / Assouline, Dan; Mohajeri, Nahid; Gudmundsson, Agust; Scartezzini, Jean-Louis.

In: Geothermal Energy, Vol. 7, 19, 25.07.2019, p. 1-50.

Research output: Contribution to journalArticlepeer-review

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Assouline, Dan ; Mohajeri, Nahid ; Gudmundsson, Agust ; Scartezzini, Jean-Louis. / A machine learning approach for mapping the very shallow theoretical geothermal potential. In: Geothermal Energy. 2019 ; Vol. 7. pp. 1-50.

BibTeX

@article{b89a2e6ae0c74183828cb7f2b2c00c38,
title = "A machine learning approach for mapping the very shallow theoretical geothermal potential",
author = "Dan Assouline and Nahid Mohajeri and Agust Gudmundsson and Jean-Louis Scartezzini",
year = "2019",
month = jul,
day = "25",
doi = "10.1186/s40517-019-0135-6",
language = "English",
volume = "7",
pages = "1--50",
journal = "Geothermal Energy",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - A machine learning approach for mapping the very shallow theoretical geothermal potential

AU - Assouline, Dan

AU - Mohajeri, Nahid

AU - Gudmundsson, Agust

AU - Scartezzini, Jean-Louis

PY - 2019/7/25

Y1 - 2019/7/25

U2 - 10.1186/s40517-019-0135-6

DO - 10.1186/s40517-019-0135-6

M3 - Article

VL - 7

SP - 1

EP - 50

JO - Geothermal Energy

JF - Geothermal Energy

M1 - 19

ER -