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

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

Abstract The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constraints regarding ground-related laws a...

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Bibliographic Details
Main Authors: Dan Assouline, Nahid Mohajeri, Agust Gudmundsson, Jean-Louis Scartezzini
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Geothermal Energy
Subjects:
Geothermal potential
Very shallow system
Geographic Information Systems
Machine learning
Switzerland
Online Access:http://link.springer.com/article/10.1186/s40517-019-0135-6

Internet

http://link.springer.com/article/10.1186/s40517-019-0135-6

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