Intelligent selection of NEO deflection strategies under uncertainty.

• Machine learning classifier to support planetary defense decision making. • Intelligent classification of deflection strategies under aleatory and epistemic uncertainty. • Combination of machine learning and Dempster-Shafer theory of evidence to robust decision making. This paper presents an Intelligent Decision Support System (IDSS) that can automatically assess the suitable robust deflection strategies to respond to a Near Earth Objects (NEO) impact scenario. The input to the IDSS is the warning time, the orbital parameters and mass of the NEO and the corresponding uncertainties. The output is the deflection strategies that are more likely to offer a successful deflection. Both aleatory and epistemic uncertainties on ephemerides and physical properties of the NEO are considered. The training data set is produced by generating thousands of virtual impactors, sampled from the current distribution of NEO. For each virtual impactor we perform a robust optimisation, under mixed aleatory/epistemic uncertainties, of the deflection scenario with different deflection strategies. The robust performance indices is considered by the deflection effectiveness, which is quantified by Probability of Collision post deflection. The IDSS is based on a combination Dempster-Shafer theory of evidence and a Random Forest classifier that is trained on the data set of virtual impactors and deflection scenarios. Five deflection strategies are modelled and included in the IDSS: Nuclear Explosion, Kinetic Impactor, Laser Ablation, Gravity Tractor and Ion Beam Shepherd. Simulation results suggest that the proposed decision support system can quickly provide robust decisions on which deflection strategies are to be chosen to respond to a NEO impact scenario. Once trained the IDSS does not require re-running expensive simulations to make decisions on which deflection strategies are to be used and is, therefore, suitable for the rapid pre-screening or reassessment of deflection options. [ABSTRACT FROM AUTHOR]