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Aprendizaje profundo híbrido basado en atención para la predicción de ozono en la capa límite utilizando perfiles

Shahab S Band1, Sultan Noman Qasem2, Javad Ramezani3

  • 1Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan.

Ecotoxicology and environmental safety
|December 20, 2025
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Resumen

El pronóstico preciso del ozono a nivel del suelo es crucial para la protección ambiental y de la salud. Este estudio presenta modelos avanzados de aprendizaje profundo, y EMD-ConvBiGRU-AttentionNet muestra la mayor precisión de predicción para el ozono en la capa límite.

Palabras clave:
inteligencia artificialmecanismo de atenciónbig dataozono en la capa límiteciencia de datosaprendizaje profundodescomposición empírica de modosaprendizaje automáticodetección remota

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Área de la Ciencia:

  • Química y física atmosférica
  • Ciencias ambientales
  • Ciencia de datos y aprendizaje automático.

Sus antecedentes:

  • El ozono a nivel del suelo es un contaminante atmosférico importante formado por reacciones fotoquímicas, que representa riesgos para la salud humana y los ecosistemas.
  • La predicción del ozono en la capa límite es un desafío debido a las complejas relaciones no lineales con factores meteorológicos y químicos, y la falta de datos verticales de alta resolución.
  • El Instrumento de Monitoreo de Ozono (OMI) proporciona datos valiosos de perfiles de ozono cruciales para mejorar los modelos de predicción.

Objetivo del estudio:

  • Evaluar la efectividad de varios modelos de aprendizaje profundo para pronosticar las concentraciones de ozono en la capa límite.
  • Desarrollar y evaluar arquitecturas novedosas de aprendizaje profundo, incluidos mecanismos de atención y descomposición empírica de modos, para mejorar la predicción de ozono.
  • Comparar el rendimiento de los modelos propuestos con los métodos convencionales utilizando métricas clave de precisión.

Principales métodos:

  • Se utilizó el producto de perfil de ozono OMPROFOZ del instrumento OMI del satélite Aura.
  • Se evaluaron redes neuronales recurrentes (RNN), redes neuronales convolucionales (CNN), unidades recurrentes con compuerta (GRU), memoria a corto y largo plazo (LSTM) y modelos híbridos (GRU-CNN, LSTM-CNN).
  • Se desarrollaron y probaron modelos avanzados: ConvBiGRU-AttentionNet y EMD-ConvBiGRU-AttentionNet, que incorporan mecanismos de atención y descomposición empírica de modos.

Principales resultados:

  • Los modelos de aprendizaje profundo propuestos superaron significativamente a los métodos convencionales en la predicción de ozono.
  • EMD-ConvBiGRU-AttentionNet demostró la mayor precisión de predicción entre todos los modelos evaluados.
  • Los análisis visuales, incluidos los diagramas de residuos y los mapas de atención, confirmaron la capacidad de los modelos para capturar patrones espacio-temporales complejos.

Conclusiones:

  • Los modelos avanzados de aprendizaje profundo, en particular EMD-ConvBiGRU-AttentionNet, ofrecen un enfoque prometedor para la predicción precisa del ozono en la capa límite.
  • La integración de mecanismos de atención y descomposición empírica de modos mejora la capacidad de los modelos para manejar datos atmosféricos complejos.
  • La mejora en la predicción de ozono puede ayudar a mitigar los efectos adversos de este importante contaminante atmosférico en la salud y el medio ambiente.