<?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>0122-3461</journal-id>
<journal-title><![CDATA[Ingeniería y Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. Desarro.]]></abbrev-journal-title>
<issn>0122-3461</issn>
<publisher>
<publisher-name><![CDATA[Fundación Universidad del Norte]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0122-34612025000100122</article-id>
<article-id pub-id-type="doi">10.14482/inde.43.01.155.454</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción de heladas y variables meteorológicas relevantes en agricultura en la Sabana de Bogotá usando machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Frost and relevant meteorological variables forecast in agriculture in the Sabana de Bogotá using machine learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[CASTILLO MÉNDEZ]]></surname>
<given-names><![CDATA[ROBINSON]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[CAMACHO CASTRO]]></surname>
<given-names><![CDATA[JULIÁN ANDRÉS]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,SENA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af2">
<institution><![CDATA[,SENA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>43</volume>
<numero>1</numero>
<fpage>122</fpage>
<lpage>139</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0122-34612025000100122&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0122-34612025000100122&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0122-34612025000100122&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen A partir de información histórica de variables climatológicas y de las heladas, es posible mejorar las decisiones tomadas en las actividades de la agricultura, buscando determinar patrones que garanticen mayor rendimiento y calidad de los cultivos e implementando modelos de predicción basados en machine learning (ML). Se propone desarrollar un modelo ML que permita determinar el comportamiento de las variables meteorológicas temperatura, pluviosidad y humedad relativa, asi como de las heladas en la Sabana de Bogotá. Se ha partido de la conformación de una base de datos históricos de estas variables desde 2010 hasta abril de 2023, considerando información de diez estaciones meteorológicas diferentes de la región. Ha sido necesario implementar técnicas de imputación de datos en los vacios de información. Para determinar el modelo con la respuesta más cercana a la realidad, se desarrolló un modelo basado en regresión lineal múltiple y otro en redes neuronales artificiales. De acuerdo con los resultados obtenidos y el nivel de error absoluto, el segundo modelo aproxima sus predicciones más cerca a los datos reales. El trabajo desarrollado puede ser una herramienta esencial para generar un sistema de alerta temprana que ayude a los agricultores de la Sabana de Bogotá.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Taking into account historical information on climatological and frost variables, it is possible to improve decisions made in agricultural activities, seeking to determine patterns that guarantee greater yield and quality of crops and implementing forecast models based on machine learning (ML). This work presents the development of a ML model that allows determining the behavior of the meteorological variables, temperature, rainfall, and relative humidity, as well as frost, in the Sabana de Bogotá. The starting point was the creation of a historical database of these variables from 2010 to April 2023, considering information from ten different meteorological stations in the region. It has been necessary to implement data imputation techniques in information gaps. To determine the model with the response closest to reality, a model based on multiple linear regression and another on artificial neural networks were developed. According to the results and the level of absolute error, the second model approximates its forecasts closer to the real data. The work developed can be an essential tool to generate an early warning system that helps farmers in the Sabana de Bogotá.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[predicción de heladas]]></kwd>
<kwd lng="es"><![CDATA[regresión lineal múltiple]]></kwd>
<kwd lng="es"><![CDATA[red neuronal de avance]]></kwd>
<kwd lng="es"><![CDATA[variables meteorológicas]]></kwd>
<kwd lng="en"><![CDATA[feedforward neural network]]></kwd>
<kwd lng="en"><![CDATA[frost forecasting]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[meteorological conditions]]></kwd>
<kwd lng="en"><![CDATA[multiple linear regression]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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