A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation
This study proposes the hybrid NEAML-BIOPASTAZA (Neutrosophic and Explainable Artificial Learning) model for Biodiversity and Legal-Ecological Assessment in Pastaza, which integrates multivariate statistical analysis, neutrosophic logic, and supervised machine learning to assess the relationship bet...
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-05-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/62.%20Relationshipbetweenenvironmentaliteracy.pdf |
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| author | Pablo Santiago López Freire Jocelyn Estefanía Morocho Hidalgo Leslye Pamela Calderón Andy Stiwer Jhostin Quiroz |
| author_facet | Pablo Santiago López Freire Jocelyn Estefanía Morocho Hidalgo Leslye Pamela Calderón Andy Stiwer Jhostin Quiroz |
| author_sort | Pablo Santiago López Freire |
| collection | DOAJ |
| description | This study proposes the hybrid NEAML-BIOPASTAZA (Neutrosophic and Explainable Artificial Learning) model for Biodiversity and Legal-Ecological Assessment in Pastaza, which integrates multivariate statistical analysis, neutrosophic logic, and supervised machine learning to assess the relationship between environmental literacy and the effectiveness of the legal framework for biodiversity conservation in the Pastaza canton. Using a database of 350 observations, exploratory factor analysis was applied to validate the latent structure of the "environmental literacy" construct, considering variables such as legal knowledge, biodiversity perception, community participation, and media exposure. To manage the uncertainty inherent in social responses, a neutrosophic model was implemented, capturing the degrees of truth (T), indeterminacy (I), and falsity (F) of each perception. Finally, a Random Forest Classifier was used to predict the level of effective conservation, identifying the most relevant factors in local ecological decision-making. The combined approach allows for a more comprehensive and explanatory view of the problem, highlighting the need to strengthen environmental education, legal implementation, and community participation as pillars for the sustainable management of Amazonian biodiversity. |
| format | Article |
| id | doaj-art-0cb30d619b8d4d0993d4ebb767f59962 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-0cb30d619b8d4d0993d4ebb767f599622025-08-24T18:04:16ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-05-0184767779A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity ConservationPablo Santiago López FreireJocelyn Estefanía Morocho HidalgoLeslye Pamela CalderónAndy Stiwer Jhostin QuirozThis study proposes the hybrid NEAML-BIOPASTAZA (Neutrosophic and Explainable Artificial Learning) model for Biodiversity and Legal-Ecological Assessment in Pastaza, which integrates multivariate statistical analysis, neutrosophic logic, and supervised machine learning to assess the relationship between environmental literacy and the effectiveness of the legal framework for biodiversity conservation in the Pastaza canton. Using a database of 350 observations, exploratory factor analysis was applied to validate the latent structure of the "environmental literacy" construct, considering variables such as legal knowledge, biodiversity perception, community participation, and media exposure. To manage the uncertainty inherent in social responses, a neutrosophic model was implemented, capturing the degrees of truth (T), indeterminacy (I), and falsity (F) of each perception. Finally, a Random Forest Classifier was used to predict the level of effective conservation, identifying the most relevant factors in local ecological decision-making. The combined approach allows for a more comprehensive and explanatory view of the problem, highlighting the need to strengthen environmental education, legal implementation, and community participation as pillars for the sustainable management of Amazonian biodiversity. https://fs.unm.edu/NSS/62.%20Relationshipbetweenenvironmentaliteracy.pdfenvironmental literacybiodiversity conservationenvironmental legal frameworkexploratory factor analysisneutrosophic logicsupervised machine learning |
| spellingShingle | Pablo Santiago López Freire Jocelyn Estefanía Morocho Hidalgo Leslye Pamela Calderón Andy Stiwer Jhostin Quiroz A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation Neutrosophic Sets and Systems environmental literacy biodiversity conservation environmental legal framework exploratory factor analysis neutrosophic logic supervised machine learning |
| title | A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation |
| title_full | A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation |
| title_fullStr | A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation |
| title_full_unstemmed | A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation |
| title_short | A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation |
| title_sort | hybrid neutrosophic and machine learning model for assessing environmental literacy in biodiversity conservation |
| topic | environmental literacy biodiversity conservation environmental legal framework exploratory factor analysis neutrosophic logic supervised machine learning |
| url | https://fs.unm.edu/NSS/62.%20Relationshipbetweenenvironmentaliteracy.pdf |
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