Prediction of Hazardous Asteroids Using Machine Learning

Title

Prediction of Hazardous Asteroids Using Machine Learning

Creator

Surekha N.P.; Nanjundan P.; Bashir S.

Description

As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of machine learning techniques. With the increasing interest in safeguarding our planet from potential impact events, the accurate classification and prediction of hazardous asteroids is of paramount importance. This research leverages a diverse dataset comprising a wide array of asteroid characteristics, including orbital parameters, physical properties, and historical impact data, to train and validate machine learning models. The study employs a combination of feature engineering, data preprocessing, and state-of-the-art machine learning algorithms to assess the risk posed by asteroids in near-Earth space. 2024 IEEE.

Source

ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings, pp. 169-174.

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Asteroid Impact; data analysis - minor planets; Machine Learning; Planetary defense; Potentially Hazardous Asteroids; Prediction; Random Forest Classification

Coverage

Surekha N.P., Christ University Pune, Department of Data Science, Lavasa, India; Nanjundan P., Christ University Pune, Department of Data Science, Lavasa, India; Bashir S., Christ University Pune, Department of Data Science, Lavasa, India

Rights

Restricted Access

Relation

ISBN: 979-835034985-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ESIC60604.2024.10481589

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190992971&doi=10.1109%2fESIC60604.2024.10481589&partnerID=40&md5=3a4c445df8078adc6d2ead14b9dd5358