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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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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
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PubMed
Resumen
Este resumen es generado por máquina.

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.

Palabras clave:
toma de decisiones inteligentes.el aprendizaje de gráficos espaciotemporales.entornos urbanos de los entornos urbanos.

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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.