<?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>S2007-24222019000100253</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2019-01-10</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Red Neuronal Artificial y series de Fourier para pronóstico de temperaturas en el Distrito de Riego 075, Sinaloa México]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial Neural Network and Fourier series to forecasting temperatures of Irrigation District 075, Sinaloa México]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cervantes-Osornio]]></surname>
<given-names><![CDATA[Rocío]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arteaga-Ramírez]]></surname>
<given-names><![CDATA[Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Peña]]></surname>
<given-names><![CDATA[Mario Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ojeda-Bustamante]]></surname>
<given-names><![CDATA[Waldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quevedo-Nolasco]]></surname>
<given-names><![CDATA[Abel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias Campo Experimental Valle de México ]]></institution>
<addr-line><![CDATA[Texcoco México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Irrigación Sección Meteorología agrícola]]></institution>
<addr-line><![CDATA[ México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Irrigación Sección Meteorología agrícola]]></institution>
<addr-line><![CDATA[ México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Mexicano de Tecnología del Agua  ]]></institution>
<addr-line><![CDATA[Jiutepec Morelos]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Colegio de Posgraduados Programa de Hidrociencias ]]></institution>
<addr-line><![CDATA[Texcoco ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>02</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>02</month>
<year>2019</year>
</pub-date>
<volume>10</volume>
<numero>1</numero>
<fpage>253</fpage>
<lpage>268</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222019000100253&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222019000100253&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222019000100253&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La temperatura es una variable trascendental en el cálculo de la evapotranspiración, el crecimiento, el desarrollo y el rendimiento de las plantas; en el estudio de la transmisión de plagas y enfermedades; en el pronóstico del clima; en la determinación del flujo de calor; en el cálculo de la presión real de vapor. Todos estos procesos son afectados por el calentamiento global. El objetivo de este trabajo es comparar los mejores resultados de dos modelos: uno de red neuronal artificial (RNA) backpropagation, y otro de series de Fourier. Se utilizaron datos diarios de temperaturas máximas (T  max ) y mínimas (T  min ) de las estaciones Santa Rosa 1, Ruiz Cortines, Batequis y Santa Rosa 2, del Distrito de Riego 075 Valle del Fuerte, Los Mochis, Sinaloa, México. En la RNA, 1 484 vectores de datos fueron utilizados para entrenamiento, validación y prueba y 229 para pronóstico. Para el entrenamiento las variables de entrada de la RNA fueron: día juliano, longitud, latitud y altitud. Se obtuvieron 96 escenarios con una, dos y tres capas ocultas, con diversos números de neuronas en cada capa oculta. Con los 1 484 datos, se obtuvieron los mejores ajustes para los modelos de series de Fourier para temperaturas máximas y mínimas, y se pronosticaron 229 datos para las cuatro estaciones. Los mejores modelos de RNA backpropagation para el pronóstico de temperaturas máximas y mínimas diarias obtuvieron desempeños similares en comparación con los realizados por los mejores modelos de series de Fourier, para las estaciones de estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Temperature is a transcendental variable in aspects such as evapotranspiration calculation, growth, development and yield of plants, in the study of the transmission pests and diseases, in the weather forecast, in determination of heat fluxes, in the calculation of the real vapor pressure, all these processes affected by global warming. The objective of this work was to compare the best results of two models: one of artificial neural network (RNA) backpropagation, and another of Fourier series. Daily data of maximum temperatures (T max ) and minimum (T min ) of the Santa Rosa 1, Ruiz Cortínes, Batequis and Santa Rosa 2 stations, of the Irrigation District 075 Valle del Fuerte, Los Mochis, Sinaloa, Mexico were used. In RNA, 1484 data vectors were used for training, validation and testing and 229 to forecasting. For training, the input variables of the RNA were: Julian day, longitude, latitude and altitude. Were obtained 96 scenarios with one, two and three hidden layers, with different numbers of neurons in each hidden layer. With the 1484 data, the best adjustments were obtained for the Fourier series models for maximum and minimum temperatures, and 229 data were predicted for the four stations. The best RNA backpropagation models for the prediction of maximum and minimum daily temperatures obtained similar performances in comparison with those made by the best models of Fourier series, for the study stations.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Pronóstico]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[temperatura máxima]]></kwd>
<kwd lng="es"><![CDATA[temperatura mínima]]></kwd>
<kwd lng="en"><![CDATA[Forecasting]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[maximum temperature]]></kwd>
<kwd lng="en"><![CDATA[minimum temperature]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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