<?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>S2007-24222017000200051</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2017-02-05</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Daily streamflow simulation based on the improved machine learning method]]></article-title>
<article-title xml:lang="es"><![CDATA[Simulación de caudales diarios mediante el método de aprendizaje automático mejorado]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Kan]]></surname>
<given-names><![CDATA[Guangyuan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[Xiaoyan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ding]]></surname>
<given-names><![CDATA[Liuqian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Jiren]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hong]]></surname>
<given-names><![CDATA[Yang]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ren]]></surname>
<given-names><![CDATA[Minglei]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lei]]></surname>
<given-names><![CDATA[Tianjie]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Liang]]></surname>
<given-names><![CDATA[Ke]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zuo]]></surname>
<given-names><![CDATA[Depeng]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Pengnian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,China Institute of Water Resources and Hydropower Research  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tsinghua University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,University of Oklahoma  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>USA</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Hohai University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Beijing Normal University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Nanjing University of Information Sciences & Technology  ]]></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>51</fpage>
<lpage>60</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222017000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222017000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222017000200051&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La simulación de caudales diarios se ha implementado por lo general mediante modelos hidrológicos distribuidos o conceptuales. En la actualidad, los datos hidrológicos, que pueden obtenerse con facilidad de sistemas automáticos de medición, son más que suficientes. Por lo tanto, el aprendizaje automático (machine learning) se ha convertido en una herramienta eficaz y popular, muy adecuada para la tarea de simulación de caudales. En este trabajo se propone un método de aprendizaje automático mejorado denominado modelo PKEK, basado en el modelo NU-PEK, previamente propuesto para generar resultados de simulación de flujo diario más precisos y estables. Los resultados de la comparación entre el modelo PKEK y el modelo NU-PEK indican que el modelo mejorado ofrece mayor exactitud y estabilidad, y tiene un excelente potencial de aplicación en la simulación de caudales diarios.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[daily streamflow simulation]]></kwd>
<kwd lng="en"><![CDATA[hydrological model]]></kwd>
<kwd lng="en"><![CDATA[flood forecasting]]></kwd>
<kwd lng="en"><![CDATA[global optimization]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[simulación de caudales diarios]]></kwd>
<kwd lng="es"><![CDATA[modelo hidrológico]]></kwd>
<kwd lng="es"><![CDATA[inundación]]></kwd>
<kwd lng="es"><![CDATA[pronósticos de inundación]]></kwd>
<kwd lng="es"><![CDATA[optimización global]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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