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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM14671

Accident Severity Prediction

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM14671

Volume:

6 |

6 |

Jun 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VKB

Varun Kumar B

RV

Reddy Vamsi

RRV

Rajeev Reddy V

SR

Sai Rohit

DS

Dr. Somashekar

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Abstract

Nowadays transport of airways has been playing a key role in every industry airline and passengers safety is the first concern we should take care of, Various Safety checks are done continuously 24/7 manually, and every safety measure and precaution are been taken care of by the airline team but still some case accidents due to various reasons like pilot error, air traffic controller error, design and manufacturer defects, maintenance failures, sabotage, or inclement weather etc. The prediction of flight accident severity is crucial for enhancing aviation safety and preventing future accidents. In this research paper, we propose a machine learning-based approach for predicting the severity of flight accidents. Our proposed approach involves the use of feature engineering techniques to extract useful information from the accident dataset, followed by the application of various machine learning algorithms to predict the accident severity. We use a publicly available dataset of flight accidents to evaluate the performance of our proposed approach

KEYWORDS

Error Correction Safety Management Flight Training Data Random Forest Airplane Crash SVM python programming

Cite This Article

Varun Kumar B, Reddy Vamsi, Rajeev Reddy V, Sai Rohit, Dr. Somashekar, "Accident Severity Prediction," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 6, Jun 2022.

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Publication Date

5/22/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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