<?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>2007-2422</journal-id>
<journal-title><![CDATA[Tecnología y ciencias del agua]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnol. cienc. agua]]></abbrev-journal-title>
<issn>2007-2422</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Tecnología del Agua, Coordinación de Comunicación, Participación e Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-24222021000300032</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2021-03-02</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estimación de la evapotranspiración de referencia con datos de temperatura: una comparación entre técnicas de cálculo convencionales y de inteligencia artificial en una región cálida-subhúmeda]]></article-title>
<article-title xml:lang="en"><![CDATA[Estimation of reference evapotranspiration from temperature data: A comparison between conventional calculation and artificial intelligence techniques in a warm-sub-humid region]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos-Cirilo]]></surname>
<given-names><![CDATA[Luis Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quej-Chi]]></surname>
<given-names><![CDATA[Victor Hugo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carrillo-Ávila]]></surname>
<given-names><![CDATA[Eugenio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aceves-Navarro]]></surname>
<given-names><![CDATA[Everardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivera-Hernández]]></surname>
<given-names><![CDATA[Benigno]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Champotón Campeche]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Champotón Campeche]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Champotón Campeche]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Champotón Campeche]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Popular de la Chontalpa  ]]></institution>
<addr-line><![CDATA[Cárdenas Tabasco]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>12</volume>
<numero>3</numero>
<fpage>32</fpage>
<lpage>81</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222021000300032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222021000300032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222021000300032&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La evapotranspiración de referencia (ETo) es un parámetro agro-meteorológico de gran importancia para muchas áreas de estudio como la geotecnia, climatología e hidrología, donde su mayor importancia recae en el cálculo de la evapotranspiración de cultivo (ETc). En el presente estudio, utilizando solamente datos de temperatura, se evaluó el desempeño de tres modelos de inteligencia artificial y dos ecuaciones convencionales para predecir la evapotranspiración de referencia (ETo) en un clima cálido subhúmedo en México. Los modelos de inteligencia artificial evaluados fueron máquinas de soporte vectorial (SVM), programación de expresión genética (GEP) y XGBoost, así como los modelos convencionales de Hargreaves- Samani y Camargo. El desempeño de los modelos se evaluó de acuerdo con los índices estadísticos error absoluto medio (MAE); raíz cuadrada media del error (RMSE); coeficiente de determinación (R2), y el error medio de sesgo (MBE). Se construyeron intervalos de confianza para cada índice estadístico utilizando la técnica de remuestreo bootstrap, con el propósito de evaluar la incertidumbre de los mismos. Los resultados demuestran que entre los modelos convencionales evaluados la ecuación de Camargo obtuvo un mejor desempeño en la estimación de la ETo en comparación con la ecuación de Hargreaves. Respecto a los modelos de inteligencia artificial, el modelo SVM obtuvo mejor desempeño entre las técnicas evaluadas. De manera general, se recomienda utilizar el modelo SVM para estimar valores de ETo al superar a las demás técnicas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Reference evapotranspiration (ETo) is an agro-meteorological parameter of great importance for many areas of study such as geotechnics, climatology and hydrology, where its greatest importance falls in the calculation of the crop&#8217;s evapotranspiration (ETc). In this study, using only temperature data, the performance of three artificial intelligence models and two conventional equations to predict the reference evapotranspiration (ETo) was evaluated in a warm sub-humid climate in México. The artificial intelligence models evaluated were: support vector machines (SVM), Gene Expression Programming (GEP) and XGBoost, and the conventional models were those by Hargreaves-Samani and Camargo. The performance of the models was evaluated according to the statistical indexes: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Mean Bias Error (MBE). Confidence intervals were constructed for each statistical index using the technique of bootstrap resampling with the purpose of evaluating their uncertainty. The results show that among the conventional models evaluated, the equation by Camargo obtained a better performance in the estimation of ETo compared to the equation by Hargreaves. Regarding the artificial intelligence models, the SVM model obtained the best performance among the techniques evaluated. In general, it is recommended to use the SVM model to estimate the ETo values since it outperforms the other techniques.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[evapotranspiración de referencia]]></kwd>
<kwd lng="es"><![CDATA[técnicas de inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[estaciones meteorológicas automatizadas]]></kwd>
<kwd lng="es"><![CDATA[bootstrap]]></kwd>
<kwd lng="en"><![CDATA[Reference evapotranspiration]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence techniques]]></kwd>
<kwd lng="en"><![CDATA[automated weather stations]]></kwd>
<kwd lng="en"><![CDATA[bootstrap]]></kwd>
</kwd-group>
</article-meta>
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