Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge

Autores
Carrasco, D.; Olivas, Jose A.; Higueras, Pablo L.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This work presents a process for developing an intelligent hybrid system designed to effectively leverage georeferenced data and expert knowledge. The effectiveness of this approach is demonstrated in this work through a specific case study, using the proposed system to achieve a powerful tool for mineral prospectivity. The system consists of three main phases: knowledge and valuable data acquisition, modeling, and results representation using prospectivity heat maps. In the initial step, the recovery and representation of expert knowledge for the case of study was conducted. This system design was tested in the Almadén Mercury Mining District, it involved interviewing expert geologists with ages of experience in the area. Afterwards, the gathering of georeferenced data was carried out to enrich the dataset. Following this phase, the modelling was done, first, using unsupervised techniques to unveil the underlying structure and patterns of the information. Later, employing supervised learning and knowledge representation techniques to enhance the results. In the final step, prospectivity maps were created to represent the achieved results to help in decision making.
Facultad de Informática
Materia
Ciencias Informáticas
Hybrid intelligent systems
Mineral Exploration
Artificial intelligence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/171710
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/171710
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Hybrid Intelligent System for Leveraging Georeferenced Data and KnowledgeCarrasco, D.Olivas, Jose A.Higueras, Pablo L.Ciencias InformáticasHybrid intelligent systemsMineral ExplorationArtificial intelligenceThis work presents a process for developing an intelligent hybrid system designed to effectively leverage georeferenced data and expert knowledge. The effectiveness of this approach is demonstrated in this work through a specific case study, using the proposed system to achieve a powerful tool for mineral prospectivity. The system consists of three main phases: knowledge and valuable data acquisition, modeling, and results representation using prospectivity heat maps. In the initial step, the recovery and representation of expert knowledge for the case of study was conducted. This system design was tested in the Almadén Mercury Mining District, it involved interviewing expert geologists with ages of experience in the area. Afterwards, the gathering of georeferenced data was carried out to enrich the dataset. Following this phase, the modelling was done, first, using unsupervised techniques to unveil the underlying structure and patterns of the information. Later, employing supervised learning and knowledge representation techniques to enhance the results. In the final step, prospectivity maps were created to represent the achieved results to help in decision making.Facultad de Informática2024-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf16-20http://sedici.unlp.edu.ar/handle/10915/171710enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2413-1info:eu-repo/semantics/reference/url/https://sedici.unlp.edu.ar/handle/10915/171300info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:18:04Zoai:sedici.unlp.edu.ar:10915/171710Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:18:04.85SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
title Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
spellingShingle Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
Carrasco, D.
Ciencias Informáticas
Hybrid intelligent systems
Mineral Exploration
Artificial intelligence
title_short Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
title_full Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
title_fullStr Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
title_full_unstemmed Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
title_sort Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
dc.creator.none.fl_str_mv Carrasco, D.
Olivas, Jose A.
Higueras, Pablo L.
author Carrasco, D.
author_facet Carrasco, D.
Olivas, Jose A.
Higueras, Pablo L.
author_role author
author2 Olivas, Jose A.
Higueras, Pablo L.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Hybrid intelligent systems
Mineral Exploration
Artificial intelligence
topic Ciencias Informáticas
Hybrid intelligent systems
Mineral Exploration
Artificial intelligence
dc.description.none.fl_txt_mv This work presents a process for developing an intelligent hybrid system designed to effectively leverage georeferenced data and expert knowledge. The effectiveness of this approach is demonstrated in this work through a specific case study, using the proposed system to achieve a powerful tool for mineral prospectivity. The system consists of three main phases: knowledge and valuable data acquisition, modeling, and results representation using prospectivity heat maps. In the initial step, the recovery and representation of expert knowledge for the case of study was conducted. This system design was tested in the Almadén Mercury Mining District, it involved interviewing expert geologists with ages of experience in the area. Afterwards, the gathering of georeferenced data was carried out to enrich the dataset. Following this phase, the modelling was done, first, using unsupervised techniques to unveil the underlying structure and patterns of the information. Later, employing supervised learning and knowledge representation techniques to enhance the results. In the final step, prospectivity maps were created to represent the achieved results to help in decision making.
Facultad de Informática
description This work presents a process for developing an intelligent hybrid system designed to effectively leverage georeferenced data and expert knowledge. The effectiveness of this approach is demonstrated in this work through a specific case study, using the proposed system to achieve a powerful tool for mineral prospectivity. The system consists of three main phases: knowledge and valuable data acquisition, modeling, and results representation using prospectivity heat maps. In the initial step, the recovery and representation of expert knowledge for the case of study was conducted. This system design was tested in the Almadén Mercury Mining District, it involved interviewing expert geologists with ages of experience in the area. Afterwards, the gathering of georeferenced data was carried out to enrich the dataset. Following this phase, the modelling was done, first, using unsupervised techniques to unveil the underlying structure and patterns of the information. Later, employing supervised learning and knowledge representation techniques to enhance the results. In the final step, prospectivity maps were created to represent the achieved results to help in decision making.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/171710
url http://sedici.unlp.edu.ar/handle/10915/171710
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2413-1
info:eu-repo/semantics/reference/url/https://sedici.unlp.edu.ar/handle/10915/171300
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
16-20
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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