<?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>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-62362024000100035</article-id>
<article-id pub-id-type="doi">10.20937/atm.53355</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Prediction of hydrological drought by the Standardized Precipitation Evapotranspiration Index in Chihuahua, Mexico, using machine learning algorithms]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Melchor Varela]]></surname>
<given-names><![CDATA[Javier Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez Hernández]]></surname>
<given-names><![CDATA[Joseph Isaac]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Chihuahua  ]]></institution>
<addr-line><![CDATA[Chihuahua Chihuahua]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>38</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362024000100035&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362024000100035&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362024000100035&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Despite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN A pesar de ser muy común que el territorio de Chihuahua, Chihuahua, México, experimente sequía, sus consecuencias continúan impactando severamente a la población sin previo aviso. El aprendizaje automático ha demostrado tener una importante capacidad para predecir series temporales, y el índice estandarizado de evapotranspiración y precipitación (SPEI, por su sigla en inglés) se perfila como el indicador de sequía más preciso. En este estudio, se desarrollaron modelos predictivos utilizando redes neuronales artificiales (ANN), memoria a largo y corto plazo (LSTM) y regresión de vectores de soporte (SVR) para estimar el SPEI. Se consideraron escalas temporales de 12 (SPEI 12) y 24 meses (SPEI 24) para el periodo 1901-2020 en el territorio mencionado. Esto se hizo para simular el comportamiento de los ciclos de sequía y mejorar la capacidad de anticipar las consecuencias. Los índices de precisión utilizados para evaluar los modelos fueron el error cuadrático medio (MSE), el error absoluto medio (MAE), el error de sesgo medio (MBE), el coeficiente de determinación (R2) y el coeficiente de Kendall. En total, se realizaron 956 experimentos con los tres métodos, variando parámetros como el número de neuronas, el kernel y el grado del polinomio, entre otros. Se seleccionaron los dos mejores modelos para cada método y los resultados promedio revelaron MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495 y coeficiente de Kendall = 0.7592 para SPEI 12, y MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218 y coeficiente de Kendall = 0.8558 para SPEI 24.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[SPEI]]></kwd>
<kwd lng="en"><![CDATA[ANN]]></kwd>
<kwd lng="en"><![CDATA[LSTM]]></kwd>
<kwd lng="en"><![CDATA[SVR]]></kwd>
<kwd lng="en"><![CDATA[drought]]></kwd>
<kwd lng="en"><![CDATA[Mexico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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