Intelligent System for Prediction of Potentially Hazardous Nearest Earth Objects Using Machine Learning
Keywords:
Asteroids, Prediction, NEOs, Machine Learning, Hazardous, Classification, Logistic RegressionAbstract
Large potentially hazardous NEOs can cause a worldwide disaster in the occasion of a planetary colliding. These collisions with Earth are not uncommon. Every year, hundreds of asteroids hit the surface of our planet, the majority of which are relatively small to cause any worry. But occasionally, big rocks can collide and harm anything. In order to categorize the population of NEAs as potentially harmful or non-hazardous, this study proposes a method that uses different algorithms to learn complicated representations that are present in the distribution of accessible asteroid orbital data.
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