<?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>2310-3469</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Forestales]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cubana ciencias forestales]]></abbrev-journal-title>
<issn>2310-3469</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Pinar del Río Hermanos Saíz Montes de Oca]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2310-34692018000100015</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Proyección de supervivencia en plantaciones de Pinus caribaea Morelet var. caribaea Barrett &amp; Golfari]]></article-title>
<article-title xml:lang="en"><![CDATA[Survival prognosis in plantations of Pinus caribaea Morelet var. caribaea Barrett &amp; Golfari]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mariel Guera]]></surname>
<given-names><![CDATA[Ouorou Ganni]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aleixo da Silva]]></surname>
<given-names><![CDATA[José Antônio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Caraciolo Ferreira]]></surname>
<given-names><![CDATA[Rinaldo Luiz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Álvarez Lazo]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barrero Medel]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Federal Rural de Pernambuco (UFRPE) Departamento de Ciência Florestal (DCFL) Laboratório de Biometria e Manejo Florestal (LBMF)]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Pinar del Río «Hermanos Saíz Montes de Oca» Facultad de Ciencias Forestales y Agropecuaria Departamento Forestal ]]></institution>
<addr-line><![CDATA[Pinar del Río ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2018</year>
</pub-date>
<volume>6</volume>
<numero>1</numero>
<fpage>15</fpage>
<lpage>30</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2310-34692018000100015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2310-34692018000100015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2310-34692018000100015&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El presente estudio fue realizado con el objetivo de obtener ecuaciones de regresión y Redes Neuronales Artificiales (RNAs) para la proyección de la supervivencia de Pinus caribaea var. caribaea en la empresa forestal Macurije, provincia de Pinar del Río, Cuba. Los datos utilizados en las modelaciones provienen de la medición de las variables edad (años) y supervivencia (densidad) en parcelas permanentes circulares de 500 m², establecidas en plantaciones de P. caribaea var. caribaea. El estudio se dividió en tres etapas: 1) Ajuste de modelos de regresión tradicionales para proyección de supervivencia; 2) Entrenamientos de RNAs para la proyección de supervivencia, incluyendo variables categóricas «sitio» y «Unidades Básicas de Producción Forestal»; 3) Comparación de los desempeños de las ecuaciones de regresión con los de las RNAs en la proyección de la supervivencia. Los mejores modelos y RNAs fueron seleccionados y basados en: coeficiente de determinación ajustado -R2 aj (%), raíz cuadrada del error medio cuadrático - RMSE (%) y análisis de distribución de residuos. La evaluación de la bondad de ajuste de los modelos también incluyó la verificación de los supuestos de normalidad, homocedasticidad y ausencia de autocorrelación serial en los residuos por las pruebas de Kolmogorov-Smirnov, White y Durbin-Watson, respectivamente. El modelo de Pienaar y Shiver en 1981 resultó ser el de mejor ajuste en la proyección de la supervivencia. La RNA de arquitectura MLP 13-10-1 fue la de mejor capacidad de generalización y presentó un desempeño similar al de la ecuación obtenida del ajuste del modelo de Pienaar y Shiver.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The present study was carried out with the objective of obtaining regression equations and Artificial Neural Networks (ANNs) for the prognosis of Pinus caribaea var. caribaea survival in Macurije Forest Company, province of Pinar del Río - Cuba. The data used in the modeling comes from the measurement of the variables age (years) and survival (density) in circular permanent plots of 500 m² established in P. caribaea var. caribaea plantations. The study was divided into three stages: i) Adjustment of survival traditional regression models; ii) Training of ANNs for survival prognosis, including categorical variables «site» and «Basic Units of Forest Production»; iii) Comparison of regression equations performance with those of ANNs in survival prognosis. The best models and ANNs were selected based on: adjusted determination coefficient - R2 aj (%), square root of the mean square error - RMSE (%) and residue distribution analysis. The evaluation of the models goodness of fit also included the verification of the assumptions of normality, homocedasticity and absence of serial autocorrelation in the residues by Kolmogorov-Smirnov, White and Durbin-Watson tests, respectively. The model of Pienaar and Shiver, in 1981 turned out to be the best fit in survival prognosis. The ANN MLP 13-10-1 was the one with the best generalization capacity and presented a performance similar to that of Pienaar and Shiver equation.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[plantaciones forestales]]></kwd>
<kwd lng="es"><![CDATA[mortalidad regular]]></kwd>
<kwd lng="es"><![CDATA[regresión no lineal]]></kwd>
<kwd lng="es"><![CDATA[Redes Neuronales Artificiales]]></kwd>
<kwd lng="en"><![CDATA[forest plantations]]></kwd>
<kwd lng="en"><![CDATA[regular mortality]]></kwd>
<kwd lng="en"><![CDATA[nonlinear regression]]></kwd>
<kwd lng="en"><![CDATA[Artificial Neural Networks (ANNs)]]></kwd>
</kwd-group>
</article-meta>
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