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...

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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

Description
Summary:	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.

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

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