| Title |
A machine learning approach for mapping the very shallow theoretical geothermal potential
|
|---|---|
| Published in |
Geothermal Energy, July 2019
|
| DOI | 10.1186/s40517-019-0135-6 |
| Authors |
Dan Assouline, Nahid Mohajeri, Agust Gudmundsson, Jean-Louis Scartezzini |
Mendeley readers
The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 72 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 18% |
| Researcher | 11 | 15% |
| Student > Master | 9 | 13% |
| Student > Doctoral Student | 5 | 7% |
| Unspecified | 5 | 7% |
| Other | 8 | 11% |
| Unknown | 21 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 13 | 18% |
| Earth and Planetary Sciences | 8 | 11% |
| Computer Science | 5 | 7% |
| Unspecified | 5 | 7% |
| Energy | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 30 | 42% |