Abstract
The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.
Highlights
Asteroids, described as small rocky bodies with sizes consisting of a few metres to a few hundred kilometres in diameter, constitute a potential threat
We have applied the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to classify hazardous near-Earth object (NEO)
We will motivate the application of Multi-Criteria Decision Making (MCDM) techniques to hazardous NEO assessment
Summary
Asteroids, described as small rocky bodies with sizes consisting of a few metres to a few hundred kilometres in diameter, constitute a potential threat. It is clear that an assessment of hazardous NEOs involves a wide list of varied nature criteria. We contribute the first known Multi-Criteria Decision Making (MCDM) approach for hazardous NEO assessment. The MCDA involves a wide range of techniques to systematically address each decision problem. These techniques facilitate a consensus regarding the final decision and the treatment of a large amount of information, which is usually expressed by various measurement magnitudes and meanings
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