A multi-model approach using XAI and anomaly detection to predict asteroid hazards
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913747555057664 |
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| author | Mondal, Amit Kumar Aslam, Nafisha Maji, Prasenjit Mondal, Hemanta Kumar |
| author_facet | Mondal, Amit Kumar Aslam, Nafisha Maji, Prasenjit Mondal, Hemanta Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A multi-model approach using XAI and anomaly detection to predict asteroid hazards Mondal, Amit Kumar Aslam, Nafisha Maji, Prasenjit Mondal, Hemanta Kumar Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning 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. |
| title | A multi-model approach using XAI and anomaly detection to predict asteroid hazards |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.15901 |