Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge

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...

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Autores principales:	Carrasco, D., Olivas, Jose A., Higueras, Pablo L.
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2024
Materias:	Ciencias Informáticas Hybrid intelligent systems Mineral Exploration Artificial intelligence
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/171710
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

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spelling	I19-R120-10915-1717102024-10-21T20:02:55Z http://sedici.unlp.edu.ar/handle/10915/171710 Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge Carrasco, D. Olivas, Jose A. Higueras, Pablo L. 2024-06 2024 2024-10-21T12:51:19Z en Ciencias Informáticas Hybrid intelligent systems Mineral Exploration Artificial intelligence 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 Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 16-20
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Hybrid intelligent systems Mineral Exploration Artificial intelligence
spellingShingle	Ciencias Informáticas Hybrid intelligent systems Mineral Exploration Artificial intelligence Carrasco, D. Olivas, Jose A. Higueras, Pablo L. Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
topic_facet	Ciencias Informáticas Hybrid intelligent systems Mineral Exploration Artificial intelligence
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.
format	Objeto de conferencia Objeto de conferencia
author	Carrasco, D. Olivas, Jose A. Higueras, Pablo L.
author_facet	Carrasco, D. Olivas, Jose A. Higueras, Pablo L.
author_sort	Carrasco, D.
title	Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge
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
publishDate	2024
url	http://sedici.unlp.edu.ar/handle/10915/171710
work_keys_str_mv	AT carrascod hybridintelligentsystemforleveraginggeoreferenceddataandknowledge AT olivasjosea hybridintelligentsystemforleveraginggeoreferenceddataandknowledge AT higueraspablol hybridintelligentsystemforleveraginggeoreferenceddataandknowledge
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Hybrid Intelligent System for Leveraging Georeferenced Data and Knowledge

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