<?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>S2007-24222018000400209</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2018-04-09</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Comparación de modelos físicos y de inteligencia artificial para predicción de niveles de inundación]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparison of physical models and artificial intelligence for prediction of flood levels]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Agudelo-Otálora]]></surname>
<given-names><![CDATA[Luis M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moscoso-Barrera]]></surname>
<given-names><![CDATA[William D.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Paipa-Galeano]]></surname>
<given-names><![CDATA[Luis A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mesa-Sciarrotta]]></surname>
<given-names><![CDATA[Catalina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de La Sabana  ]]></institution>
<addr-line><![CDATA[Chía Cundinamarca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de La Sabana  ]]></institution>
<addr-line><![CDATA[Chía Cundinamarca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de La Sabana  ]]></institution>
<addr-line><![CDATA[Chía Cundinamarca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de La Sabana  ]]></institution>
<addr-line><![CDATA[Chía Cundinamarca]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2018</year>
</pub-date>
<volume>9</volume>
<numero>4</numero>
<fpage>209</fpage>
<lpage>235</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222018000400209&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222018000400209&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222018000400209&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La hidrología ha utilizado métodos tradicionales para pronosticar niveles de inundación. Sin embargo, éstos pueden generar problemas de precisión, causados por el comportamiento no lineal de las inundaciones y las limitaciones al no incluir todas las variables, como flujo, y nivel de agua y precipitación. En consecuencia, algunos científicos comenzaron a utilizar métodos no convencionales basados en modelos de inteligencia artificial, pronosticando las inundaciones de manera más precisa y rigurosa. Este artículo presenta una comparación de un modelo de tránsito de flujo unidimensional desarrollado en HEC-RAS y un modelo de inteligencia artificial, basado en redes neuronales artificiales, desarrollado en MatLab, para predecir inundaciones. El análisis de los resultados se llevó a cabo utilizando seis indicadores estadísticos: error absoluto medio (MAE, por su nombre en inglés); error cuadrático medio (MSE); error medio porcentual absoluto (MAPE, por su nombre en inglés); raíz cuadrada de la MSE; coeficiente de correlación de Pearson (CC, por su nombre en inglés), y coeficiente de correlación de concordancia (&#961;c, por su nombre en inglés). Además, el coeficiente de eficiencia se calculó empleando una herramienta virtual llamada Hydrotest. A partir del análisis se observó en los modelos de pronóstico que el uso de redes neuronales tiene resultados precisos, dada su cercanía con los datos reales: MAPE, entre 11.95 y 12.51; CC, entre 0.90 y 0.92; &#961;c, entre 0.84 y 0.87, y finalmente un CE más grande que 0.8. El estudio se realizó en una sección de las partes altas del río Bogotá, en Colombia, entre las estaciones hidrológicas de puente Florencia y Tocancipá. Los datos de flujo fueron tomados por la Corporación Autónoma Regional de Cundinamarca (CAR) de septiembre de 2009 a octubre de 2013.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Hydrology has used traditional methods for flood level forecasting. However, this type of forecast can lead to accuracy issues, caused by the nonlinear behavior of floods and limitations by not including all variables, such as water flow, level and precipitation. Consequently, some scientists began to use unconventional methods based on artificial intelligence models, to forecast floods more precisely and rigorously. This paper compares the HEC-RAS one-dimensional flow transit model with an artificial intelligence model based on Artificial Neural Networks, developed in MatLab to predict floods. The results were analyzed using six statistical indicators: mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), square root of the MSE, Pearson correlation coefficient (CC), and concordance correlation coefficient (&#961;c). In addition, the efficiency coefficient was calculated, and used in a virtual tool called Hydrotest. The analysis shows that forecast models that use neural networks have accurate results, given their closeness to the real data: MAPE between 11.95 and 12.51, CC between 0.90 and 0.92, &#961;c between 0.84 and 0.87, and a coefficient of efficiency larger than 0.8. The study was conducted on a section of the upper Bogotá River, in Colombia, between the Florence Bridge and Tocancipá hydrological stations. Flow data was taken from the Regional Autonomous Corporation of Cundinamarca (CAR), from September 2009 to October 2013.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[HEC-RAS]]></kwd>
<kwd lng="es"><![CDATA[modelo físico]]></kwd>
<kwd lng="es"><![CDATA[modelo inteligente]]></kwd>
<kwd lng="es"><![CDATA[pronóstico de inundaciones]]></kwd>
<kwd lng="en"><![CDATA[Artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[HEC-RAS]]></kwd>
<kwd lng="en"><![CDATA[physical model]]></kwd>
<kwd lng="en"><![CDATA[intelligent model]]></kwd>
<kwd lng="en"><![CDATA[flood forecasting]]></kwd>
</kwd-group>
</article-meta>
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