New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s42452-021-04600-w http://hdl.handle.net/11449/206293 |
Resumo: | Abstract: Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract: [Figure not available: see fulltext.] |
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New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater managementArtificial intelligence in geosciencesComputer science in geosciencesData ethicsAbstract: Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract: [Figure not available: see fulltext.]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)AgroiconeBiosystems Engineering Department School of Sciences and Engineering São Paulo State University (UNESP)Biosystems Engineering Department School of Sciences and Engineering São Paulo State University (UNESP)FAPESP: 2014/04524-7FAPESP: 2016/09737-4CNPq: 421782/2016-1AgroiconeUniversidade Estadual Paulista (Unesp)de Oliveira Ferreira Silva, CésarMatulovic, Mariana [UNESP]Lilla Manzione, Rodrigo [UNESP]2021-06-25T10:29:40Z2021-06-25T10:29:40Z2021-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s42452-021-04600-wSN Applied Sciences, v. 3, n. 6, 2021.2523-3971http://hdl.handle.net/11449/20629310.1007/s42452-021-04600-w2-s2.0-85105228492Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSN Applied Sciencesinfo:eu-repo/semantics/openAccess2021-10-23T03:03:37Zoai:repositorio.unesp.br:11449/206293Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T03:03:37Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
title |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
spellingShingle |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management de Oliveira Ferreira Silva, César Artificial intelligence in geosciences Computer science in geosciences Data ethics |
title_short |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
title_full |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
title_fullStr |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
title_full_unstemmed |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
title_sort |
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management |
author |
de Oliveira Ferreira Silva, César |
author_facet |
de Oliveira Ferreira Silva, César Matulovic, Mariana [UNESP] Lilla Manzione, Rodrigo [UNESP] |
author_role |
author |
author2 |
Matulovic, Mariana [UNESP] Lilla Manzione, Rodrigo [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Agroicone Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
de Oliveira Ferreira Silva, César Matulovic, Mariana [UNESP] Lilla Manzione, Rodrigo [UNESP] |
dc.subject.por.fl_str_mv |
Artificial intelligence in geosciences Computer science in geosciences Data ethics |
topic |
Artificial intelligence in geosciences Computer science in geosciences Data ethics |
description |
Abstract: Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract: [Figure not available: see fulltext.] |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:29:40Z 2021-06-25T10:29:40Z 2021-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s42452-021-04600-w SN Applied Sciences, v. 3, n. 6, 2021. 2523-3971 http://hdl.handle.net/11449/206293 10.1007/s42452-021-04600-w 2-s2.0-85105228492 |
url |
http://dx.doi.org/10.1007/s42452-021-04600-w http://hdl.handle.net/11449/206293 |
identifier_str_mv |
SN Applied Sciences, v. 3, n. 6, 2021. 2523-3971 10.1007/s42452-021-04600-w 2-s2.0-85105228492 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SN Applied Sciences |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
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1803649432092147712 |