<?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>1026-8774</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias geológicas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. cienc. geol]]></abbrev-journal-title>
<issn>1026-8774</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geología]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1026-87742021000100043</article-id>
<article-id pub-id-type="doi">10.22201/cgeo.20072902e.2021.1.1605</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de inteligencia artificial y técnicas de aprendizaje automático para la evaluación de la susceptibilidad por movimientos en masa]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of Artificial Intelligence and machine learning techniques for landslide susceptibility assessment]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ospina-Gutiérrez]]></surname>
<given-names><![CDATA[Juan Pablo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aristizábal]]></surname>
<given-names><![CDATA[Edier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas Departamento de Geociencias y Medio Ambiente]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2021</year>
</pub-date>
<volume>38</volume>
<numero>1</numero>
<fpage>43</fpage>
<lpage>54</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1026-87742021000100043&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1026-87742021000100043&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1026-87742021000100043&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los movimientos en masa son uno de los fenómenos de origen natural con mayores pérdidas humanas y económicas alrededor del mundo, por lo que la evaluación de la susceptibilidad y amenaza es una herramienta fundamental para la ordenación de territorios. Existe en la reciente literatura una amplia gama de algoritmos de Inteligencia Artificial con aproximaciones diversas para evaluar y zonificar la susceptibilidad por movimiento en masa. En el presente estudio se implementaron diferentes algoritmos de Aprendizaje Automático para la cuenca de la quebrada La Miel, en los Andes colombianos, con el objetivo de evaluar el desempeño y la capacidad de predicción entre los diferentes modelos. Los resultados obtenidos para este caso de estudio arrojan que los modelos ensamblados tipo boosting presentan los mejores valores en términos de desempeño y capacidad de predicción, contrastando con los modelos paramétricos lineales y señalando las limitaciones de éstos en la modelización de problemas complejos, como los movimientos en masa.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Landslides are one of the most naturally occurring phenomena with the highest human and economic losses around the world, reason for the susceptibility and hazard assessment is a fundamental tool for land use planning. There is a wide range of Artificial Intelligence algorithms in the recent literature with completely different approaches to establish the relationship between the independent variable (predictors) and the dependent variable (landslide inventory). In the present study, a wide range of algorithms were used for the La Miel creek basin, in the Colombian Andes, and the methodology implemented for this type of data-based modeling is presented in detail and step by step. The results obtained show that the assembled boosting models present the best values in terms of performance and predictability. Contrasting with the linear parametric models, pointing out their limitations in modeling complex problems such as landslides.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[movimientos en masa]]></kwd>
<kwd lng="es"><![CDATA[susceptibilidad]]></kwd>
<kwd lng="es"><![CDATA[métodos ensamblados]]></kwd>
<kwd lng="es"><![CDATA[modelos lineales]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[Andes colombianos]]></kwd>
<kwd lng="en"><![CDATA[landslide]]></kwd>
<kwd lng="en"><![CDATA[susceptibility]]></kwd>
<kwd lng="en"><![CDATA[assembled methods]]></kwd>
<kwd lng="en"><![CDATA[linear models]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[colombian Andes]]></kwd>
</kwd-group>
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