Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists?
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-59320-9_93 http://hdl.handle.net/11449/206132 |
Resumo: | In this modern and dynamic society, threatened by climate change, poverty, hungry and economical systems collapse, artificial intelligence (AI) emerged as a promise field to solve many actual problems. Although AI does not give absolute answers. The outputs of AI methods are subjective and in many situations depend on human-based decisions. It has a strong impact on decision-making processes and geoscientists are highly exposed to this question. Specifically, on groundwater, issues involving water quality and water quantity deserve special attention for monetary resources applications, urban supply, ecosystemical services should be balanced in order to avoid biased solutions. This paper aims to present some AI methods and discuss where it they can lead geoscientists with and without an ethical posture. A study case using monitoring water levels data is presented. |
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Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists?Algorithmic responsibilityData analysisGeoethicsGeosciencesIn this modern and dynamic society, threatened by climate change, poverty, hungry and economical systems collapse, artificial intelligence (AI) emerged as a promise field to solve many actual problems. Although AI does not give absolute answers. The outputs of AI methods are subjective and in many situations depend on human-based decisions. It has a strong impact on decision-making processes and geoscientists are highly exposed to this question. Specifically, on groundwater, issues involving water quality and water quantity deserve special attention for monetary resources applications, urban supply, ecosystemical services should be balanced in order to avoid biased solutions. This paper aims to present some AI methods and discuss where it they can lead geoscientists with and without an ethical posture. A study case using monitoring water levels data is presented.Biosystems Engineering Department School of Sciences and Engineering (DEB/FCE) São Paulo State University (UNESP)Biosystems Engineering Department School of Sciences and Engineering (DEB/FCE) São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Manzione, Rodrigo Lilla [UNESP]Matulovic, Mariana [UNESP]2021-06-25T10:27:04Z2021-06-25T10:27:04Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart441-445http://dx.doi.org/10.1007/978-3-030-59320-9_93Advances in Science, Technology and Innovation, p. 441-445.2522-87222522-8714http://hdl.handle.net/11449/20613210.1007/978-3-030-59320-9_932-s2.0-85103542565Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAdvances in Science, Technology and Innovationinfo:eu-repo/semantics/openAccess2021-10-22T21:09:44Zoai:repositorio.unesp.br:11449/206132Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:28:35.761443Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
title |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
spellingShingle |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? Manzione, Rodrigo Lilla [UNESP] Algorithmic responsibility Data analysis Geoethics Geosciences |
title_short |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
title_full |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
title_fullStr |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
title_full_unstemmed |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
title_sort |
Decision-Making in Groundwater Management: Where Artificial Intelligence Can Really Lead Geoscientists? |
author |
Manzione, Rodrigo Lilla [UNESP] |
author_facet |
Manzione, Rodrigo Lilla [UNESP] Matulovic, Mariana [UNESP] |
author_role |
author |
author2 |
Matulovic, Mariana [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Manzione, Rodrigo Lilla [UNESP] Matulovic, Mariana [UNESP] |
dc.subject.por.fl_str_mv |
Algorithmic responsibility Data analysis Geoethics Geosciences |
topic |
Algorithmic responsibility Data analysis Geoethics Geosciences |
description |
In this modern and dynamic society, threatened by climate change, poverty, hungry and economical systems collapse, artificial intelligence (AI) emerged as a promise field to solve many actual problems. Although AI does not give absolute answers. The outputs of AI methods are subjective and in many situations depend on human-based decisions. It has a strong impact on decision-making processes and geoscientists are highly exposed to this question. Specifically, on groundwater, issues involving water quality and water quantity deserve special attention for monetary resources applications, urban supply, ecosystemical services should be balanced in order to avoid biased solutions. This paper aims to present some AI methods and discuss where it they can lead geoscientists with and without an ethical posture. A study case using monitoring water levels data is presented. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:27:04Z 2021-06-25T10:27:04Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-59320-9_93 Advances in Science, Technology and Innovation, p. 441-445. 2522-8722 2522-8714 http://hdl.handle.net/11449/206132 10.1007/978-3-030-59320-9_93 2-s2.0-85103542565 |
url |
http://dx.doi.org/10.1007/978-3-030-59320-9_93 http://hdl.handle.net/11449/206132 |
identifier_str_mv |
Advances in Science, Technology and Innovation, p. 441-445. 2522-8722 2522-8714 10.1007/978-3-030-59320-9_93 2-s2.0-85103542565 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Advances in Science, Technology and Innovation |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
441-445 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128516946919424 |