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Predicción y Mitigación de Riesgos de Dispositivos Radiológicos de Dispersión Desplegados por Drones Utilizando

Osamong Gideon Akou¹, Xuan Wang¹, Shuhuan Liu¹

¹School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, China.

Risk analysis : an official publication of the Society for Risk Analysis

|January 30, 2026

Resumen

Los dispositivos radiológicos de dispersión (RDD) desplegados por drones plantean una amenaza a la seguridad. Nuestro marco integra modelado, optimización y aprendizaje automático para evaluar los riesgos de RDD e informar la preparación para emergencias.

Área de la Ciencia:

Seguridad Nacional
Seguridad Radiológica
Modelado Computacional

Sus antecedentes:

El despliegue de drones de dispositivos radiológicos de dispersión (RDD) presenta un desafío de seguridad significativo y en evolución.
Las herramientas existentes requieren mejoras para una evaluación efectiva de las consecuencias y la planificación de la respuesta contra amenazas novedosas.

Objetivo del estudio:

Desarrollar y validar un marco computacional integrado para evaluar amenazas de RDD basadas en drones.
Identificar escenarios adversarios óptimos y evaluar las consecuencias potenciales para una preparación de emergencia informada.

Principales métodos:

Marco integrado que combina modelado de dispersión basado en física, optimización restringida (L-BFGS-B) y aprendizaje automático.
Simulaciones de Monte Carlo que generan 2000 escenarios sintéticos con radionucleidos, meteorología y zonas de riesgo variadas.
Modelado de consecuencias utilizando HotSpot y JRODOS, seguido de análisis de acciones protectoras.

Principales resultados:

La optimización identificó compensaciones entre la efectividad de la dispersión (maximización de la contaminación >10 km²) y el sigilo operativo (minimización del riesgo de energía/detección).
HotSpot predijo una dosis efectiva total (TED) y una concentración de aire integrada en el tiempo (TIAC) más altas en comparación con JRODOS.
Se identificaron el I-131 y el Am-241 como radionucleidos clave para los riesgos agudos y a largo plazo, respectivamente.
El refugio reforzado reduce significativamente la dosis acumulativa.

Conclusiones:

El marco desarrollado proporciona una herramienta robusta para evaluar amenazas de RDD basadas en drones e informar las estrategias de respuesta a emergencias.
Los modelos de aprendizaje automático ofrecen predicciones rápidas y precisas (R² = 0.975), siendo la distancia el predictor principal.
Los hallazgos ofrecen orientación práctica para mejorar las medidas de seguridad nacional y protección pública contra amenazas radiológicas.

Palabras clave:

aprendizaje automático Monte Carlo RDD TED riesgo

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