Machine Learning in Planetary Defence Early Warning Systems for Hazardous Asteroids

Machine Learning in Planetary Defence Early Warning Systems for Hazardous Asteroids

K. Praneeth Reddy

Department of CSE (AI&ML)

2111cs020352@mallareddyuniversity.ac.in

M. Praneeth

Department of CSE (AI&ML)

2111cs020354@mallareddyuniversity.ac.in

G. Praneeth Sai Saran

Department of CSE (AI&ML)

2111cs020353@mallareddyuniversity.ac.in

V. Prabhas
Department of CSE(AI&ML)

2111cs020355@mallareddyuniversity.ac.in

Dr. D. Thiyagarajan

Department of CSE (AI&ML)

School of Engineering

MALLA REDDY UNIVERSITY

HYDERABAD

Abstract: Asteroid hazard prediction is a critical area of study in space technology, aiming to safeguard Earth from potential catastrophic collisions. With the increasing detection of near-Earth objects (NEOs), it is essential to develop advanced predictive models that can accurately assess the likelihood and severity of asteroid impacts. This research introduces a novel machine learning based model designed to analyse key asteroid parameters, including dimensions, speed, trajectory, and atmospheric conditions, to predict potential threats with high precision. The proposed system leverages advanced algorithms, particularly XGBoost, to process astronomical data obtained from telescopes and satellites, achieving an impressive classification accuracy of 99.99%. By training the model on extensive historical asteroid collision data and simulating plausible impact scenarios, the system provides valuable insights into impact probabilities, mitigation strategies, and early warning mechanisms. Compared to traditional methods relying on orbital mechanics and impact modelling, the machine learning approach offers improved efficiency, real-time processing capabilities, and greater adaptability to new data. Furthermore, the integration of real-time observational data enhances the accuracy of predictions, ensuring timely responses to potential threats. The research emphasizes the importance of global collaboration in asteroid monitoring, advocating for a networked system that enables seamless data sharing between space agencies and research institutions worldwide. Future developments in this domain aim to refine trajectory predictions through enhanced data assimilation techniques, incorporate deep learning methodologies to improve model accuracy, and deploy the system in real-time applications for planetary defense initiatives. By bridging the gap between traditional impact assessment techniques and cutting-edge artificial intelligence, this study contributes to a more robust and scalable approach for asteroid hazard prediction, ultimately strengthening Earth’s defense against extraterrestrial threats.

Keywords:  Asteroid hazard prediction, Near-Earth objects (NEOs), Machine learning, XGBoost, Predictive models, Astronomical data, Trajectory analysis, Impact probability, Classification accuracy, Atmospheric conditions, Collision simulation, Real-time processing.