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Aprendizaje de gráficos espacio-temporales en entornos urbanos
Hewen Li1, Linlin Hou1, Jing Cui1
1State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.
Environmental science & technology
|February 10, 2026
Resumen
El aprendizaje de gráficos espacio-temporales (STGL) ofrece un enfoque novedoso para modelar dinámicas urbanas complejas, mejorando la inteligencia y el pronóstico ambientales. Esta revisión sintetiza los avances de STGL para sistemas urbanos resilientes y adaptativos.
Área de la Ciencia:
- Ciencias del medio ambiente Ciencias del medio ambiente.
- Ciencias de la computación Ciencias de la computación
- Planificación Urbana El planeamiento urbano.
Sus antecedentes:
- Los entornos urbanos exhiben dinámicas complejas y no lineales que involucran agua, suelo, aire e infraestructura.
- Los enfoques de modelado tradicionales luchan por capturar estas intrincadas interacciones espacio-temporales.
- El aprendizaje de gráficos espacio-temporales (STGL) presenta un poderoso marco para analizar la complejidad urbana.
Objetivo del estudio:
- Proporcionar la primera revisión integral del Aprendizaje de Gráficos Espacio-temporales (STGL) específicamente diseñado para entornos urbanos.
- Para sintetizar los avances recientes en STGL, incluyendo la construcción de gráficos, modelado y estrategias de fusión.
- Examinar las diversas aplicaciones de STGL en diversos sistemas y desafíos urbanos.
Principales métodos:
- Revisión sistemática de la literatura de aprendizaje de gráficos espaciotemporales (STGL) centrada en aplicaciones urbanas.
- Análisis de técnicas de construcción de gráficos, enfoques de modelado espacial y temporal y estrategias de fusión de datos.
- Examen de estudio de caso de las implementaciones destacadas de STGL en inteligencia ambiental urbana.
Principales resultados:
- STGL modela eficazmente las dinámicas no lineales y no euclidianas en los sistemas urbanos.
- Los avances en la construcción y modelado de gráficos mejoran la precisión de las predicciones y el apoyo a la toma de decisiones.
- Aplicaciones exitosas demostradas en el agua urbana, el suelo, la calidad del aire y la gestión de riesgos.
Conclusiones:
- STGL es una tecnología fundamental para la inteligencia ambiental en entornos urbanos.
- Las direcciones futuras incluyen el aprendizaje federado, el desaprendizaje automático y el metaaprendizaje para mejorar STGL.
- Los marcos STGL de próxima generación apoyarán entornos urbanos más resistentes y adaptables.
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