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...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2017 |
| Country: | Colombia |
| Institution: | Universidad Nacional de Colombia |
| Repository: | Repositorio UN |
| Language: | Spanish |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/60164 |
| Online Access: | https://repositorio.unal.edu.co/handle/unal/60164 http://bdigital.unal.edu.co/58184/ |
| Access Level: | Open access |
| Keyword: | 55 Ciencias de la tierra / Earth sciences and geology Redes neuronales artificiales Deforestación Predicción |
| 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. |
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