Asteroid Hazard Prediction Using Machine Learning: A Comparative Analysis of Different Algorithms

The prediction of asteroid risk is a crucial topic in the realm of space technology. The utilization of machine learning techniques has enabled the identification of asteroids in photographic and video data obtained from telescopes and satellites, prediction of their trajectory, simulation of their potential impact on Earth, assessment of the probability and magnitude of such an impact, and provision of support to decision-makers in the event of an impact. The implementation of these techniques can enhance our comprehension and preparedness for prospective asteroid collisions, along with our capacity to effectively rank initiatives and distribute resources targeted at mitigating the gravity of such an impact. Furthermore, the utilization of these techniques can enhance our readiness for prospective collisions with asteroids. The present study introduces a novel model for predicting asteroid hazards, which incorporates multiple variables such as asteroid dimensions, speed, path, and atmospheric conditions. The employed methodology involves the utilization of machine learning algorithms to scrutinize past records of asteroid collisions and replicate plausible impact scenarios. The training process involves utilizing 70% of the available data for training the model, while the remaining 30% is reserved for testing purposes. By implementing these methods in current research, researchers have achieved a noteworthy accuracy of 99.99% in the task of classifying asteroids. The model is subjected to comparative analysis with alternative methods, and it exhibits superior precision in comparison to the latter.