<?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>2215-2504</journal-id>
<journal-title><![CDATA[Revista Forestal Mesoamericana Kurú]]></journal-title>
<abbrev-journal-title><![CDATA[Kurú]]></abbrev-journal-title>
<issn>2215-2504</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-25042022000200064</article-id>
<article-id pub-id-type="doi">10.18845/rfmk.v19i45.6326</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo de clasificación en diferentes estratos forestales en un entorno de llanura aluvial utilizando redes neuronales artificiales]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification model in different forest strata in a floodplain environment using artificial neural networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[Anthoinny Vittória dos Santos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Souza]]></surname>
<given-names><![CDATA[Rodrigo Galvão Teixeira de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Liarte]]></surname>
<given-names><![CDATA[Gabriel Victor Caetano Carvalho]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pinho]]></surname>
<given-names><![CDATA[Bianca Caterine Piedade]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Oliveira]]></surname>
<given-names><![CDATA[Cinthia Pereira de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gonzáles]]></surname>
<given-names><![CDATA[Duberlí Geomar Elera]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lima]]></surname>
<given-names><![CDATA[Robson Borges de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Abreu]]></surname>
<given-names><![CDATA[Jadson Coelho de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Federal dos Vales do Jequitinhonha e Mucuri Departamento de Engenharia Florestal ]]></institution>
<addr-line><![CDATA[Diamantina Minas Gerais]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade do estado do Amapá Laboratório de Manejo Florestal ]]></institution>
<addr-line><![CDATA[Macapá Amapá]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Federal do Pará  ]]></institution>
<addr-line><![CDATA[Parauapebas Pará]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Nacional Autónoma de Chota Escuela Profesional de Ingeniería Forestal y Ambiental ]]></institution>
<addr-line><![CDATA[Cajamarca ]]></addr-line>
<country>Perú</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>19</volume>
<numero>45</numero>
<fpage>64</fpage>
<lpage>70</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-25042022000200064&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-25042022000200064&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-25042022000200064&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La selva amazónica presenta diferentes estratos forestales, debido a su estructura heterogénea. En el que estos estratos pueden variar en altos, medios y bajos. El conocimiento de los diferentes patrones de estructuras verticales que se encuentran en el bosque es extremadamente importante para comprender la dinámica de la vegetación, lo que influye en las estrategias de conservación del bosque. Con el fin de optimizar el proceso de clasificación de los diferentes tipos de estratos, el objetivo del presente trabajo fue utilizar redes neuronales artificiales (RNA) para clasificar estos estratos. Se utilizaron dos algoritmos de resilient propagation (Rprop+ y Rprop-), en cuatro configuraciones diferentes de variables de entrada. El entrenamiento y la prueba de los ocho modelos de RNAs se realizaron utilizando el software R. Los modelos se evaluaron mediante una matriz de confusión. En qué modelos con entradas: HT, DAP y QF; HT, DAP y solo HT del algoritmo Rprop + obtuvieron 100% de aciertos en la clasificación de estrato, presentando una alta tasa de aprendizaje, confiabilidad y generalización de datos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The Amazon forest presents different forest strata, due to its heterogeneous structure. In which these strata can vary in upper, middle and lower. Knowledge about the different patterns of vertical structures found in the forest is extremely important for understanding the vegetation dynamics, influencing forest conservation strategies. In order to optimize the process of classifying the different types of strata, the objective of the present work was to use artificial neural networks (ANNs) to classify these strata. Two resilient propagation algorithms (Rprop + and Rprop-) were used, in four different configurations of input variables. The training and testing of the eight RNA models were performed using the R software. The models were evaluated using a confusion matrix. In which models with inputs: HT, DAP and QF; HT, DAP and only HT from the Rprop + algorithm obtained 100% correct answers in the classification of strata. Demonstrating a high rate of learning, reliability and generalization of data.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[estratificación]]></kwd>
<kwd lng="es"><![CDATA[resilient propagation.]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[resilient propagation]]></kwd>
<kwd lng="en"><![CDATA[stratification]]></kwd>
</kwd-group>
</article-meta>
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