Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal

Detalhes bibliográficos
Autor(a) principal: Nascimento, Matheus Lopes do
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134617
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Investigation of Geothermal Potential Zones with Machine Learning in Mainland PortugalGeographical Information SystemsSpatial analysisMachine LearningRandom ForestsExtreme Gradient BoostingGeothermal explorationGeothermal gradientHeat flowDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesEarth’s internal heat is explored to produce electricity or used directly in industrial processes or residencies. It is considered to be renewable and cleaner than fossil fuels and has great importance to pursue environmental goals. The exploration phase of geothermal resources is complex and expensive. It requires field surveys, geological, geophysical and geochemical analysis, as well as drilling campaigns. Geospatial data and technologies have been used to target promising sites for further investigations, and helped reduce costs while also pointed to important criteria data related to geothermal potential. Machine learning is a data driven set of technologies that has been successfully used to model environmental parameters, and in the field of geothermal energy it has been used to predict thermal properties of the surface and subsurface. Random Forests and Extreme Gradient Boosting are ensemble machine learning algorithms that have been extensively used in environmental and geological sciences, and have been demonstrated to perform well when predicting thermal properties. This study investigated a methodology that coupled GIS and ML to predict two crucial parameters in geothermal exploration throughout Mainland Portugal: Geothermal gradient and surface Heat flow density. Training data consisted in different types of wells drilled in the study area where the two labels were measured. It was provided by Portugal’s Geology and Energy Laboratory. Features were all publicly available and consisted in geological, hydrogeological, geophysical, weather and terrain data. Data were aggregated in two grids with two spatial resolutions. The results between the two algorithms have been compared and discussed. The most important features that contributed to the models were identified and their relationships with the outputs discussed. The models and the prediction maps over the study area showed the location of zones with higher geothermal gradient and surface heat flow density and can be used to aid geothermal exploration and provide insights for geothermal modelling.Henriques, Roberto André PereiraTang, Vicente de AzevedoFiliberto, Pla BañónRUNNascimento, Matheus Lopes do2022-03-16T14:05:19Z2022-03-022022-03-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/134617TID:202966054enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:13:03Zoai:run.unl.pt:10362/134617Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:10.480706Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
title Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
spellingShingle Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
Nascimento, Matheus Lopes do
Geographical Information Systems
Spatial analysis
Machine Learning
Random Forests
Extreme Gradient Boosting
Geothermal exploration
Geothermal gradient
Heat flow
title_short Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
title_full Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
title_fullStr Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
title_full_unstemmed Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
title_sort Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal
author Nascimento, Matheus Lopes do
author_facet Nascimento, Matheus Lopes do
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
Tang, Vicente de Azevedo
Filiberto, Pla Bañón
RUN
dc.contributor.author.fl_str_mv Nascimento, Matheus Lopes do
dc.subject.por.fl_str_mv Geographical Information Systems
Spatial analysis
Machine Learning
Random Forests
Extreme Gradient Boosting
Geothermal exploration
Geothermal gradient
Heat flow
topic Geographical Information Systems
Spatial analysis
Machine Learning
Random Forests
Extreme Gradient Boosting
Geothermal exploration
Geothermal gradient
Heat flow
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2022
dc.date.none.fl_str_mv 2022-03-16T14:05:19Z
2022-03-02
2022-03-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/134617
TID:202966054
url http://hdl.handle.net/10362/134617
identifier_str_mv TID:202966054
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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