  Uncontrolled spacecraft will disintegrate and generate a large amount of
debris in the reentry process, and ablative debris may cause potential risks to
the safety of human life and property on the ground. Therefore, predicting the
landing points of spacecraft debris and forecasting the degree of risk of
debris to human life and property is very important. In view that it is
difficult to predict the process of reentry process and the reentry point in
advance, and the debris generated from reentry disintegration may cause ground
damage for the uncontrolled space vehicle on expiration of service. In this
paper, we adopt the object-oriented approach to consider the spacecraft and its
disintegrated components as consisting of simple basic geometric models, and
introduce three machine learning models: the support vector regression (SVR),
decision tree regression (DTR) and multilayer perceptron (MLP) to predict the
velocity, longitude and latitude of spacecraft debris landing points for the
first time. Then, we compare the prediction accuracy of the three models.
Furthermore, we define the reentry risk and the degree of danger, and we
calculate the risk level for each spacecraft debris and make warnings
accordingly. The experimental results show that the proposed method can obtain
high accuracy prediction results in at least 15 seconds and make safety level
warning more real-time.
