eprintid: 10200262 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/20/02/62 datestamp: 2024-12-12 14:52:39 lastmod: 2024-12-12 14:52:39 status_changed: 2024-12-12 14:52:39 type: article metadata_visibility: show sword_depositor: 699 creators_name: Assouline, Dan creators_name: Mohajeri, Nahid creators_name: Gudmundsson, Agust creators_name: Scartezzini, Jean-Louis title: A machine learning approach for mapping the very shallow theoretical geothermal potential ispublished: pub divisions: UCL divisions: B04 divisions: C04 divisions: F34 keywords: Geothermal potential, Very shallow system, Geographic Information Systems, Machine learning, Switzerland note: 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. 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 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 × 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. date: 2019-07-25 date_type: published publisher: SPRINGER official_url: http://dx.doi.org/10.1186/s40517-019-0135-6 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1907913 doi: 10.1186/s40517-019-0135-6 lyricists_name: Mohajeri, Nahid lyricists_id: NMOHA02 actors_name: Mohajeri, Nahid actors_id: NMOHA02 actors_role: owner funding_acknowledgements: [Swiss Innovation Agency Innosuisse]; P300P2 174514 [Swiss National Science Foundation under Mobility Fellowship]; P300P2_174514 [Swiss National Science Foundation (SNF)] full_text_status: public publication: Geothermal Energy volume: 7 article_number: 19 pages: 50 issn: 2195-9706 citation: Assouline, Dan; Mohajeri, Nahid; Gudmundsson, Agust; Scartezzini, Jean-Louis; (2019) A machine learning approach for mapping the very shallow theoretical geothermal potential. Geothermal Energy , 7 , Article 19. 10.1186/s40517-019-0135-6 <https://doi.org/10.1186/s40517-019-0135-6>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10200262/1/s40517-019-0135-6.pdf