融合XAI与异常检测，提升小行星撞击风险预测精度。
A multi-model approach using XAI and anomaly detection to predict asteroid hazards
- 用多模型融合提取尺寸、速度等关键参数进行预测
- 结合蒙特卡洛模拟，碰撞概率评估更可靠
- 支持实时预警，适合行星防御领域应用
近地小行星（NEAs）的潜在撞击灾难性后果引发广泛关注。行星防御依赖于对潜在危险小行星（PHAs）的准确分类，但数据复杂性制约了传统方法。本文提出一种融合机器学习、深度学习、可解释AI（XAI）与异常检测的先进预测方法。通过分析历史与实时小行星数据，提取尺寸、速度、轨道等关键参数，构建混合算法以提升预测精度。预测模块可推演未来行为，蒙特卡洛模拟用于评估碰撞可能性。实时警报系统可向全球监测站发出通知，实现及时应对。该方法结合实时预警与精密建模，显著增强行星防御能力。
原文摘要 · Abstract (English)
The potential for catastrophic collision makes near-Earth asteroids (NEAs) a serious concern. Planetary defense depends on accurately classifying potentially hazardous asteroids (PHAs), however the complexity of the data hampers conventional techniques. This work offers a sophisticated method for accurately predicting hazards by combining machine learning, deep learning, explainable AI (XAI), and anomaly detection. Our approach extracts essential parameters like size, velocity, and trajectory from historical and real-time asteroid data. A hybrid algorithm improves prediction accuracy by combining several cutting-edge models. A forecasting module predicts future asteroid behavior, and Monte Carlo simulations evaluate the likelihood of collisions. Timely mitigation is made possible by a real-time alarm system that notifies worldwide monitoring stations. This technique enhances planetary defense efforts by combining real-time alarms with sophisticated predictive modeling.
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