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Arquitectura de Múltiples Agentes Consciente del Contexto para la Obtención de Información sobre Incendios Forestales

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Resumen
Este resumen es generado por máquina.

Este estudio presenta un sistema de IA que analiza diversos datos sobre incendios forestales para obtener mejores perspectivas. Mejora la toma de decisiones para una gestión y prevención eficaces de los incendios forestales.

Palabras clave:
inteligencia artificialgestión de desastresRAG multimodalsostenibilidadrespuesta visual a preguntas

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Área de la Ciencia:

  • Ciencias Ambientales; Inteligencia Artificial; Ciencias de la Computación

Sus antecedentes:

  • Los incendios forestales representan amenazas globales significativas, que se intensifican debido al cambio climático y las actividades humanas.
  • Los métodos actuales de análisis de incendios forestales a menudo procesan los datos de forma aislada, lo que dificulta el razonamiento integral y la transparencia.
  • La gestión eficaz de los incendios forestales requiere información integrada para la detección, predicción y evaluación de riesgos.

Objetivo del estudio:

  • Desarrollar un novedoso sistema multiagente (MAS) basado en orquestador para transformar datos ambientales multimodales en inteligencia procesable sobre incendios forestales.
  • Crear un marco de razonamiento transparente y consciente del contexto utilizando Modelos Multimodales Grandes (LMM) y Generación Aumentada por Recuperación (RAG).
  • Establecer un sistema de vanguardia de Respuesta Visual a Preguntas (VQA) para mejorar el apoyo a la toma de decisiones sobre incendios forestales.

Principales métodos:

  • Se diseñó un sistema multiagente (MAS) basado en orquestador que integra Modelos Multimodales Grandes (LMM).
  • Se empleó ingeniería de indicaciones estructurada y canalizaciones especializadas de Generación Aumentada por Recuperación (RAG) para un razonamiento avanzado.
  • Se desarrolló un sistema de Respuesta Visual a Preguntas (VQA) que ingiere diversos datos: imágenes de satélite, lecturas de sensores, datos meteorológicos y metraje terrestre.

Principales resultados:

  • El sistema logró una precisión de 0,797 y una puntuación F1 de 0,736 en conjuntos de datos públicos sobre incendios forestales.
  • Demostró la capacidad de ingerir y procesar datos ambientales multimodales para un análisis coherente.
  • Proporcionó un razonamiento transparente y consciente del contexto para las consultas de los usuarios relacionadas con incidentes de incendios forestales.

Conclusiones:

  • El sistema propuesto basado en IA Agentic ofrece una solución humanocéntrica para la gestión de incendios forestales.
  • Capacita a bomberos, gobiernos e investigadores con inteligencia procesable para mitigar eficazmente las amenazas de incendios forestales.
  • Destaca el potencial de la IA multimodal para abordar desafíos ambientales complejos como los incendios forestales.