<?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>S2007-24222017000200127</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2017-02-12</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration]]></article-title>
<article-title xml:lang="es"><![CDATA[Máquina de aprendizaje extremo secuencial en línea mejorada para la simulación de la evapotranspiración de referencia diaria]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Yubin]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[Zhengying]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Lei]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[Qinyin]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[Jun]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Xian Jiaotong University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>China</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2017</year>
</pub-date>
<volume>8</volume>
<numero>2</numero>
<fpage>127</fpage>
<lpage>140</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222017000200127&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222017000200127&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222017000200127&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: The traditional extreme learning machine has significant disadvantages, including slow training, difficulty in selecting parameters, and difficulty in setting the singularity and the data sample. A prediction model of an improved Online Sequential Extreme Learning Machine (IOS-ELM) of daily reference crop evapotranspiration is therefore examined in this paper. The different manipulation of the inverse of the matrix is made according to the optimal solution and using a regularization factor at the same time in the model. The flexibility of the IOS-ELM in ET0 modeling was assessed using the original meteorological data (Tmax, Tm, Tmin, n, Uh, RHm, &#966;, Z) of the years 1971-2014 in Yulin, Ankang, Hanzhong, and Xi&#8217;an of Shaanxi, China. Those eight parameters were used as the input, while the reference evapotranspiration values were the output. In addition, the ELM, LSSVM, Hargreaves, Priestley-Taylor, Mc Cloud and IOS-ELM models were tested against the FAO-56 PM model by the performance criteria. The experimental results demonstrate that the performance of IOS-ELM was better than the ELM and LSSVM and significantly better than the other empirical models. Furthermore, when the total ET0 estimation of the models was compared by the relative error, the results of the intelligent algorithms were better than empirical models at rates lower than 5%, but the gross ET0 empirical models mainly had 12% to 64.60% relative error. This research could provide a reference to accurate ET0 estimation by meteorological data and give accurate predictions of crop water requirements, resulting in intelligent irrigation decisions in Shaanxi.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La máquina de aprendizaje extremo tradicional tiene desventajas significativas, tales como entrenamiento lento, dificultad en la selección de parámetros y dificultad en establecer la singularidad y la muestra de datos. Por lo tanto, en el presente artículo se examina un modelo de predicción de una máquina de aprendizaje extremo secuencial en línea mejorada (IOS-ELM) de la evapotranspiración de referencia diaria de cultivos. La diferente manipulación de la inversa de la matriz se hace de acuerdo con la solución óptima y utilizando un factor de regularización al mismo tiempo en el modelo. La flexibilidad de la IOS-ELM en la modelación de la ET0 se evaluó empleando los datos meteorológicos originales (Tmax, Tm, Tmin, n, Uh, RHm, &#966;, Z) de los años 1971-2014 en Yulin, Ankang, Hanzhong, y Xi&#8217;an en Shaanxi, China. Estos ocho parámetros se usaron como insumos o datos de entrada, mientras que los valores de la evapotranspiración de referencia fueron los datos de salida o el producto. Asimismo, se probaron los modelos ELM, LSSVM, Hargreaves, Priestley-Taylor, Mc Cloud y IOS-ELM contra el modelo FAO-56 PM mediante los criterios de desempeño. Los resultados experimentales demuestran que el desempeño de IOS-ELM fue mejor que le de ELM y LSSVM y significativamente mejor que los demás modelos empíricos. Más aún, al comparar la estimación total de ET0 de los modelos mediante el error relativo, los resultados de los algoritmos inteligentes fueron mejores que los modelos empíricos a índices inferiores a 5%, pero los modelos empíricos de ET0 bruta tuvieron un error relativo de 12 a 64.60%. Esta investigación podría proporcionar una referencia para la estimación precisa de ET0 mediante datos meteorológicos y proporcionar predicciones precisas de los requerimientos de agua de los cultivos, lo cual resultaría en decisiones de riego inteligentes en Shaanxi.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Daily reference evapotranspiration]]></kwd>
<kwd lng="en"><![CDATA[extreme learning machine]]></kwd>
<kwd lng="en"><![CDATA[online learning]]></kwd>
<kwd lng="en"><![CDATA[matrix singularity]]></kwd>
<kwd lng="es"><![CDATA[evapotranspiración de referencia diaria]]></kwd>
<kwd lng="es"><![CDATA[máquina de aprendizaje extremo]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje en línea]]></kwd>
<kwd lng="es"><![CDATA[singularidad de la matriz]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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