Reimagining Asteroid Risk Assessment: A Comparative Review of Advanced Machine Learning Techniques

The escalating discovery rate of Near-Earth Asteroids (NEAs) has intensified the need for advanced computational frameworks capable of evaluating their impact risks with high precision. Traditional machine learning models, while foundational for early NEA classification and trajectory prediction, increasingly falter when confronted with the intricate, high-dimensional dynamics of asteroid motion. This limitation underscores the necessity for sophisticated techniques that reconcile computational efficiency with predictive accuracy across large, multi-dimensional datasets. This review systematically evaluates state-of-the-art machine learning algorithms—including quantum-enhanced models, hybrid quantum-classical frameworks, and lightweight convolutional neural networks (CNNs)—for their efficacy in asteroid risk assessment. By analyzing outcomes from recent studies, we contrast performance metrics such as accuracy, computational cost, and scalability. For instance, Quantum K-Nearest Neighbors (QKNN) demonstrates a 15% accuracy improvement over classical counterparts in high-dimensional data classification, while XGBoost achieves 99.99% precision in asteroid diameter prediction. Lightweight CNNs, such as MobileNetV1, further enable real-time processing on resource-constrained platforms like CubeSats, reducing latency by 30%.