TY  - JOUR
TI  - A machine learning approach for mapping the very shallow theoretical geothermal potential
KW  - Geothermal potential
KW  -  Very shallow system
KW  -  Geographic Information
Systems
KW  -  Machine learning
KW  -  Switzerland
UR  - http://dx.doi.org/10.1186/s40517-019-0135-6
JF  - Geothermal Energy
EP  - 50
AV  - public
ID  - discovery10200262
N1  - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
SN  - 2195-9706
VL  - 7
PB  - SPRINGER
Y1  - 2019/07/25/
A1  - Assouline, Dan
A1  - Mohajeri, Nahid
A1  - Gudmundsson, Agust
A1  - Scartezzini, Jean-Louis
N2  - 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 and policies. We propose a methodology to extract the theoretical vSGP (installed in the uppermost 10 m of the ground, and mostly at depths of 1?2 m) at the national scale for Switzerland, based on a combination of Geographic Information Systems, traditional modelling, and machine learning (ML). The theoretical vSGP is based on the estimation of three thermal characteristics of the ground that impact significantly the geothermal potential, namely the monthly temperature at various depths in the surface layer, the thermal conductivity, and the thermal diffusivity. Each of the three variables is estimated separately, to a depth of 1 m below the surface, using the following general strategy: (1) collect significant data related to the variable, (2) if not existing, extract values for the variable at available locations with the help of traditional models and part of the data as input for these models, (3) train a ML model (with the Random Forests algorithm) using the extracted variable values as examples (training output labels) and related information contained in the data as features (training input samples), (4) use the trained ML model to estimate the variable in unknown locations, (5) estimate the uncertainty attached to the estimations. The methodology estimates values for (200 в 200) (m2) pixels forming a grid over Switzerland. The strategy, however, can be generalized to any country with significant data (topographic, weather, and surface layer/soil data) available. The results indicate a very non-negligible potential for very shallow geothermal systems in Switzerland.
ER  -