<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532017000200068</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v84n201.54310</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects]]></article-title>
<article-title xml:lang="es"><![CDATA[Aplicación de redes neuronales artificiales en la modelación de la deforestación asociada a nuevos proyectos de infraestructura vial]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez-Ossa]]></surname>
<given-names><![CDATA[Luisa Fernanda]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Botero-Fernández]]></surname>
<given-names><![CDATA[Verónica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>84</volume>
<numero>201</numero>
<fpage>68</fpage>
<lpage>73</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532017000200068&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532017000200068&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532017000200068&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract 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 &#8220;Variante Porce&#8221; road and the &#8220;El Bagre-San Jacinto del Cauca&#8221; 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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen 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.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[deforestation]]></kwd>
<kwd lng="en"><![CDATA[roads.]]></kwd>
<kwd lng="es"><![CDATA[Redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[predicción]]></kwd>
<kwd lng="es"><![CDATA[deforestación]]></kwd>
<kwd lng="es"><![CDATA[vías]]></kwd>
</kwd-group>
</article-meta>
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