Hybrid Approach for Accurate Asteroid Path Prediction and Classification Using Ensemble Learning
Year: 2025
Type: article
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
DOI: 10.55041/ijsrem44035
Authors: Rhushikesh Gulave, Shashank Itagi, Nikita Shete, Swapnali Jadhav, Nanda.S. Kulkarni
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409276283