A multi-model approach using XAI and anomaly detection to predict asteroid hazards
This work addresses the critical need for improved planetary defense against asteroid collisions, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of accurately classifying potentially hazardous asteroids (NEAs) by developing a multi-model approach that combines machine learning, deep learning, XAI, and anomaly detection to predict asteroid hazards, resulting in a system that includes real-time alarms for timely mitigation.
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.