Learned Prediction for Space Debris Collision Risk and Avoidance

There are over 23000 pieces of debris, ranging in size from miniscule particles to larger meteor-sized particles, present in Earth's orbit. Although these particles may appear to be small in size, at high speeds of 15700 mph, they pose catastrophic threats to current operating satellites and future orbital missions. This research study proposes a random forest machine learning classification model that is tasked with accurately predicting the level of risk of various satellite collisions. Through training and testing a model, an accuracy rate of 98.24% is achieved. Additionally, using decision trees, the variables and qualities of satellites are assessed and ranked in terms of determining the final risk of a satellite collision. This research is a suitable asset to modern Space Situational Awareness (SSA), as improved accuracy of predicting risk of space debris collisions is crucial to the future of space debris collision warning systems.