Machine Learning and Ensemble Models for Hazardous Asteroids Prediction

Title

Machine Learning and Ensemble Models for Hazardous Asteroids Prediction

Creator

Sajid, Sadiya; Das, Shreyashi; Kokatnoor, Sujatha Arun

Description

The prediction of hazardous asteroids near Earth is critical for planetary defense and avoiding any possible impacts. This study investigates the use of five ensemble models, XGBoost, Gradient Boost, CatBoost, Voting Classifier, and Random Forest, as well as four standalone machine learning models, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Decision Tree, to improve the prediction accuracy of identifying potentially hazardous asteroids. With 92% accuracy and 91% precision, Random Forest performed better than other models. It was the preferred choice for predicting hazardous asteroids because of its capacity to handle the hugedatasetwith efficiency and its ability tomanage non-linear data patterns. Additionally, XGBoost and CatBoost providedhigh accuracy at lowcomputational costs, making them suitable for real-time monitoring. KNN, on the other hand, did not perform well, and SVM's high processing time made it less useful. In particular, Random Forest ensemble modelperformed better at predicting hazardous asteroids. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.145-155

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Asteroids prediction; CatBoost; Decision trees; Gradient boost; Hazardous objects; K-Nearest Neighbors (KNN); Logistic regression; Nearest Earth Objects (NEOs); Random forest; Support Vector Machine (SVM); Voting classifier; XGBoost

Coverage

Sajid S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Das S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981964882-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-4883-2_12

https://www.scopus.com/pages/publications/105010645162?origin=resultslist