Technology Category
Humans and AI: Explainable AI (XAI) for Human UnderstandingMachine Learning: Hardware-aware MLIntelligent Robots: Multi-Robot Systems
A multi-model approach using XAI and anomaly detection to predict asteroid hazards
🤖 AI Summary
This study addresses critical challenges in near-Earth asteroid (NEA) impact risk early warning—namely, low classification accuracy, poor interpretability, and delayed alerts for potentially hazardous asteroids (PHAs). We propose an eXplainable AI (XAI)-driven multi-model collaborative framework. Methodologically, it integrates an LSTM-GNN hybrid model for spatiotemporal feature learning, SHAP/LIME-based post-hoc interpretability analysis, Isolation Forest for streaming anomaly detection, and Monte Carlo orbital simulation for probabilistic risk assessment, enabling end-to-end real-time prediction and alerting. Our key contribution is the first joint modeling of high-fidelity, attribution-aware PHA classification and dynamic trajectory-based risk estimation. Evaluated on the NASA CNEOS dataset, the system achieves 98.7% PHA identification accuracy, a 42% average improvement in lead time, and a false alarm rate below 0.3%. It has been deployed at three global monitoring stations to support real-time planetary defense decision-making.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Predict asteroid hazards using multi-model approach
Classify potentially hazardous asteroids accurately
Enable timely mitigation with real-time alarm system
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combines machine learning, deep learning, XAI, anomaly detection
Uses hybrid algorithm for improved prediction accuracy
Implements real-time alarm system for timely mitigation
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