用机器学习分类危险小行星，提升地球撞击预警能力
Hazardous Asteroids Classification
- 对比10种模型，融合5种机器学习与5种深度学习方法
- 在两个数据集上测试，最高准确率达92.3%
- 适合从事天体监测与灾害预防的研究者参考
危险小行星是人类关注的焦点，因其坠落地球可能造成巨大社会影响。监测这些天体有助于预测未来撞击事件，但大量近地天体的存在阻碍了有效监控。本项目旨在利用机器学习与深度学习技术，实现对危险小行星的精准分类。共训练并评估了十种方法，包括五种机器学习算法和五种深度学习模型。实验基于两个数据集：一个来自Kaggle，另一个从NASA提供的NeoWS（近地天体网络服务）接口提取，该服务每日更新。模型在不同特征条件下进行测试，以筛选出最适配的分类方案。结果表明，所选模型在两组数据上均表现良好，最高准确率达到92.3%。
原文摘要 · Abstract (English)
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.
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