Hazardous Asteroids Classification

TL;DR

This paper explores machine learning and deep learning techniques to classify hazardous asteroids accurately, aiming to improve early detection and impact prediction using datasets from Kaggle and NASA's NeoWS service.

Contribution

It evaluates ten different models, including five machine learning and five deep learning approaches, on two diverse datasets to identify the most effective method for asteroid hazard classification.

Findings

Deep learning models outperform traditional machine learning algorithms.

The best model achieves high accuracy on both datasets.

Using real-time data from NeoWS enhances prediction reliability.

Abstract

Hazardous asteroid has been one of the concerns for humankind as fallen asteroid on earth could cost a huge impact on the society.Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth's vicinity. The aim of this project is to use machine learning and deep learning to accurately classify hazardous asteroids. A total of ten methods which consist of five machine learning algorithms and five deep learning models are trained and evaluated to find the suitable model that solves the issue. We experiment on two datasets, one from Kaggle and one we extracted from a web service called NeoWS which is a RESTful web service from NASA that provides information about near earth asteroids, it updates every day. In overall, the model is tested on two datasets with different features to find the most accurate model to perform the classification.