Aplicación del aprendizaje automático para la predicción de la corrosión atmosférica en la zona metropolitana de la Ciudad de México
DOI:
https://doi.org/10.5935/jetia.v10i48.1089Resumen
En este trabajo se aplica Machine Learning (ML) para predecir la corrosión atmosférica en la Zona Metropolitana de la Ciudad de México. Para ello se mide la pérdida de masa como variable dependiente asociada a las variables independientes humedad relativa, tiempo de humectación, temperatura y tiempo de deposición de dióxido de azufre en 12 estaciones del sitio de estudio y con ello se genera una base de datos.
Se utilizan modelos ML con algunas herramientas de aprendizaje supervisado, tales como: Redes Neuronales (NN), Árboles de Regresión (RT), Árbol de Regresión Optimizado (ORT), Conjunto de Regresión (RE), Máquina de Vectores de Soporte (SVM) y Regresión Lineal (LR). Para este problema, las Redes Neuronales (NN) obtienen los mejores resultados, con un Coeficiente de Correlación R2 = 0,9814 y un Error Cuadrático Medio MSE = 37,9.
Los principales resultados permiten determinar que el marco propuesto puede ampliarse para predecir el comportamiento de otros problemas complejos.
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