Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects

Tropical deforestation is an ongoing process mainly caused by the construction of new roads, which, without proper environmental planning, contribute to biodiversity loss. Given that the artificial neural networks (ANNs) have the ability to capture nonlinear relationships, they were used to predict...

Full description

Autores:
Gómez-Ossa, Luisa Fernanda
Botero Fernández, Verónica
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60164
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60164
http://bdigital.unal.edu.co/58184/
Palabra clave:
55 Ciencias de la tierra / Earth sciences and geology
Redes neuronales artificiales
Deforestación
Predicción
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_affa9d99d04c57c4e4c8a6ea1250b686
oai_identifier_str oai:repositorio.unal.edu.co:unal/60164
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez-Ossa, Luisa Fernanda6439d863-5d73-43ce-98dd-532ac00683bd300Botero Fernández, Verónica5a96ec0f-4c32-4d4d-84a1-0ee28ea48dc03002019-07-02T17:40:55Z2019-07-02T17:40:55Z2017-06ISSN: 0012-7353https://repositorio.unal.edu.co/handle/unal/60164http://bdigital.unal.edu.co/58184/Tropical deforestation is an ongoing process mainly caused by the construction of new roads, which, without proper environmental planning, contribute to biodiversity loss. Given that the artificial neural networks (ANNs) have the ability to capture nonlinear relationships, they were used to predict deforestation associated with new roads, such as the “Variante Porce” road and the “El Bagre-San Jacinto del Cauca” road in the department of Antioquia. ANN Training was carried out online using the back-propagation algorithm, part of the R software. The predictive capacity was evaluated using the area under the receiver operator characteristic curve (AUC). Also, a network that showed the best predictive capacity for the deforestation surface was generated for the baseline scenario and the simulated scenario incorporating the new roads. The comparison of scenarios suggested that new roads would increase the probability of deforestation for approximately 103.729 ha of forest.La deforestación tropical es un proceso continuo causado principalmente por la construcción de nuevas vías, las cuales sin una planificación ambiental adecuada contribuyen a la pérdida de biodiversidad. Dado que las redes neuronales artificiales (RNAs) tienen la capacidad de capturar relaciones no lineales, se utilizaron para predecir la deforestación asociada a nuevas vías, como la Variante Porce y la vía El Bagre-San Jacinto del Cauca, en el departamento de Antioquia. El entrenamiento de las RNAs se realizó en modo on line con el algoritmo de retropropagación, en el software R. La capacidad de predicción se evaluó con el área bajo la curva ROC (AUC) y con la red que presentó mejor capacidad predictiva se generó la superficie de deforestación para el escenario base y el escenario simulado incorporando las nuevas vías. La comparación de escenarios indica que las nuevas vías incrementarían la probabilidad de deforestación de aproximadamente 103.729 ha de bosque.application/pdfspaUniversidad Nacional de Colombia Sede Medellín. Facultad de Minashttps://revistas.unal.edu.co/index.php/dynaUniversidad Nacional de Colombia Sede Medellín Facultad de Minas Escuela de Geociencias y Medio AmbienteEscuela de Geociencias y Medio AmbienteGómez-Ossa, Luisa Fernanda and Botero Fernández, Verónica (2017) Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects. Dyna, 84 (201). pp. 68-73. ISSN 0012-735355 Ciencias de la tierra / Earth sciences and geologyRedes neuronales artificialesDeforestaciónPredicciónApplication of artificial neural networks in modeling deforestation associated with new road infrastructure projectsArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINALDyna 2017 No. 201-68.pdfapplication/pdf692379https://repositorio.unal.edu.co/bitstream/unal/60164/1/Dyna%202017%20No.%20201-68.pdf6b3e66cdb4d6ba71513ad35aa9720791MD51THUMBNAILDyna 2017 No. 201-68.pdf.jpgDyna 2017 No. 201-68.pdf.jpgGenerated Thumbnailimage/jpeg9531https://repositorio.unal.edu.co/bitstream/unal/60164/2/Dyna%202017%20No.%20201-68.pdf.jpg70798e0f3ec0156ba1804cc567f010acMD52unal/60164oai:repositorio.unal.edu.co:unal/601642024-04-12 23:10:13.59Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
title Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
spellingShingle Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
55 Ciencias de la tierra / Earth sciences and geology
Redes neuronales artificiales
Deforestación
Predicción
title_short Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
title_full Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
title_fullStr Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
title_full_unstemmed Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
title_sort Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects
dc.creator.fl_str_mv Gómez-Ossa, Luisa Fernanda
Botero Fernández, Verónica
dc.contributor.author.spa.fl_str_mv Gómez-Ossa, Luisa Fernanda
Botero Fernández, Verónica
dc.subject.ddc.spa.fl_str_mv 55 Ciencias de la tierra / Earth sciences and geology
topic 55 Ciencias de la tierra / Earth sciences and geology
Redes neuronales artificiales
Deforestación
Predicción
dc.subject.proposal.spa.fl_str_mv Redes neuronales artificiales
Deforestación
Predicción
description Tropical deforestation is an ongoing process mainly caused by the construction of new roads, which, without proper environmental planning, contribute to biodiversity loss. Given that the artificial neural networks (ANNs) have the ability to capture nonlinear relationships, they were used to predict deforestation associated with new roads, such as the “Variante Porce” road and the “El Bagre-San Jacinto del Cauca” road in the department of Antioquia. ANN Training was carried out online using the back-propagation algorithm, part of the R software. The predictive capacity was evaluated using the area under the receiver operator characteristic curve (AUC). Also, a network that showed the best predictive capacity for the deforestation surface was generated for the baseline scenario and the simulated scenario incorporating the new roads. The comparison of scenarios suggested that new roads would increase the probability of deforestation for approximately 103.729 ha of forest.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017-06
dc.date.accessioned.spa.fl_str_mv 2019-07-02T17:40:55Z
dc.date.available.spa.fl_str_mv 2019-07-02T17:40:55Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.spa.fl_str_mv ISSN: 0012-7353
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/60164
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/58184/
identifier_str_mv ISSN: 0012-7353
url https://repositorio.unal.edu.co/handle/unal/60164
http://bdigital.unal.edu.co/58184/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/dyna
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín Facultad de Minas Escuela de Geociencias y Medio Ambiente
Escuela de Geociencias y Medio Ambiente
dc.relation.references.spa.fl_str_mv Gómez-Ossa, Luisa Fernanda and Botero Fernández, Verónica (2017) Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects. Dyna, 84 (201). pp. 68-73. ISSN 0012-7353
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín. Facultad de Minas
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/60164/1/Dyna%202017%20No.%20201-68.pdf
https://repositorio.unal.edu.co/bitstream/unal/60164/2/Dyna%202017%20No.%20201-68.pdf.jpg
bitstream.checksum.fl_str_mv 6b3e66cdb4d6ba71513ad35aa9720791
70798e0f3ec0156ba1804cc567f010ac
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814089681484120064

Publicaciones similares