Hybrid Approach for Accurate Asteroid Path Prediction and Classification Using Ensemble Learning

Hybrid Approach for Accurate Asteroid Path Prediction and Classification Using Ensemble Learning

Rhushikesh Gulave1, Shashank Itagi2, Nikita Shete3, Swapnali Jadhav4, Prof. Nanda.S.Kulkarni5

1,2,3,4Department of Computer Engineering, Siddhant College of Engineering

5Head of Department of Computer Engineering, Siddhant College of Engineering

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - This study presents a novel hybrid methodology that integrates classical physics-based orbital propagation with machine learning (ML) corrections to forecast the future trajectories of near-Earth asteroids while also assessing their hazard potential. The approach combines an analytical two-body orbit model with ML-based error correction—implemented using Long Short-Term Memory (LSTM) network—to significantly reduce prediction errors. Additionally, ensemble learning techniques using XGBoost and Random Forest are employed for asteroid hazard classification, achieving an accuracy of 99.08%. The results demonstrate that the hybrid approach not only improves long-term trajectory prediction accuracy but also enhances risk assessment, paving the way for more reliable planetary defense and space mission planning.

Key Words:  Asteroid trajectory prediction, orbital mechanics, hybrid model, machine learning, neural networks, LSTM, XGBoost, Random Forest, ensemble classification, hazard assessment