ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
Low Cost₹1501 / $59
CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Flight Accident Risk Prediction Using Machine Learning
Authors
Nashita Gazi , Suma Rani P , Srushti , Sneha , Sara Tahniyath
Keywords
Aviation Safety, Deep Learning, 1D Convolutional Neural Network, Risk Prediction, Artificial Intelligence
Abstract
Aviation safety is a critical concern due to the complex and high-risk nature of flight operations. Although aviation technology has advanced significantly, accident prevention systems are still largely reactive, relying on post-incident investigations. With the availability of large volumes of aviation and environmental data, artificial intelligence techniques can be utilized to predict potential risks before flight operations. This paper presents an AI-based flight accident risk prediction system using a One-Dimensional Convolutional Neural Network (1D-CNN). The proposed system analyzes multiple factors such as weather conditions, aircraft characteristics, flight duration, and pilot experience to estimate accident risk levels. Data preprocessing techniques including normalization and categorical encoding are applied to improve model performance. The trained CNN model effectively captures complex non-linear relationships among features and classifies flights into high-risk and low-risk categories with associated probability scores. The system is deployed through a Flask-based web application that allows real-time and manual risk prediction. Experimental results indicate that the proposed approach provides reliable predictive performance and supports proactive aviation safety management.
Full Paper, Downloads & Preview
How To Cite
Choose the style your journal or department asks for, then copy it. Every version below is generated from this paper's own record.
IJCRT — journal style
"Flight Accident Risk Prediction Using Machine Learning", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.13, Issue 12, page no.h76-h79, December 2025, Available :https://ijcrt.org/papers/IJCRT2512798.pdf
APA — 7th edition
Gazi, N., P, S. R., Srushti, Sneha, & Tahniyath, S. (2025). Flight Accident Risk Prediction Using Machine Learning. International Journal of Creative Research Thoughts, 13(12), h76-h79. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798
MLA — 9th edition
Gazi, Nashita, et al. "Flight Accident Risk Prediction Using Machine Learning." International Journal of Creative Research Thoughts, vol. 13, no. 12, 2025, pp. h76-h79, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798.
Chicago — 17th, bibliography
Gazi, Nashita, et al. "Flight Accident Risk Prediction Using Machine Learning." International Journal of Creative Research Thoughts 13, no. 12 (2025): h76-h79. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798.
Harvard — author–date
Gazi, N. et al. (2025) 'Flight Accident Risk Prediction Using Machine Learning', International Journal of Creative Research Thoughts, 13(12), pp. h76-h79. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798
IEEE — numbered reference
N. Gazi, S. R. P, Srushti, Sneha and S. Tahniyath, "Flight Accident Risk Prediction Using Machine Learning," IJCRT, vol. 13, no. 12, pp. h76-h79, Dec. 2025.
Vancouver — biomedical
Gazi N, P SR, Srushti, Sneha, Tahniyath S. Flight Accident Risk Prediction Using Machine Learning. IJCRT. 2025 Dec;13(12):h76-h79.
AMA — 11th edition
Gazi N, P SR, Srushti, Sneha, Tahniyath S. Flight Accident Risk Prediction Using Machine Learning. IJCRT. 2025;13(12):h76-h79. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2512798,
author = {Nashita Gazi and Suma Rani P and Srushti and Sneha and Sara Tahniyath},
title = {Flight Accident Risk Prediction Using Machine Learning},
journal = {International Journal of Creative Research Thoughts},
volume = {13},
number = {12},
pages = {h76-h79},
year = {2025},
month = {December},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Gazi, Nashita
AU - P, Suma Rani
AU - Srushti
AU - Sneha
AU - Tahniyath, Sara
TI - Flight Accident Risk Prediction Using Machine Learning
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 13
IS - 12
PY - 2025
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798
SP - h76-h79
ER -
Issue
Volume 13 Issue 12,
December-2025
Pages : h76-h79
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 13, Issue 12, December 2025
- Pages
- h76-h79
- Published
- December 2025
- Registration ID
- IJCRT_299423
- Published Paper ID
- IJCRT2512798
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Raichur, Karnataka, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 158
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512798
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2512798.pdf
Summarise this Paper with AI
Opens your assistant with a prepared prompt. AI summaries can be incomplete or wrong — read the full paper before citing it.
Author Services
Certificates, printed copies and official reports for this paper
Published authors can request a hard copy of their certificate or of the printed journal, and obtain the transparent peer review and plagiarism reports for this article — each issued against this paper’s registration record.
Registration ID IJCRT_299423Printed copies
Hard Copy of CertificatePrinted certificate for each author, posted to you.
Hard Copy of JournalA bound printed copy of the issue this paper appears in.
Reports & recognition
Transparent Peer Review Report
The reviewer comments and decisions behind this article.
Turnitin Plagiarism Report
The similarity check run on this manuscript.
Best Paper Nomination
Put this article forward for the issue’s best paper award.
Requests are verified against the registration ID of this article.
About Publisher
International Journal of Creative Research Thoughts
Published by IJCRT Publication
2320-2882
ISSN
9.17
Impact Factor
2013
ESTD Year
Open
Access
Impact Factor 9.17, calculated from Google Scholar citation data — see how it is worked out.
An international scholarly open access journal: peer-reviewed, refereed, multidisciplinary, monthly and multilingual, with Crossref DOI registration, rich metadata and a citation generator. Verified indexing & discovery records.
IJCRT is a Transparent, Peer-Reviewed, Open Access Journal (Refereed Journal). Following the discontinuation of UGC and UGC-CARE journal listing, IJCRT is aligned with UGC’s Suggested Parameters for Choosing Peer-Reviewed Journals. UGC Public Notice
Licence
© 2025 — Authors hold the copyright of this article.
This work is licensed under a
Creative Commons Attribution 4.0 International License
and The Open Definition.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Disclaimer:
The content, data and findings in this article are based on the authors’ research and have been
peer-reviewed for academic purposes only. Readers are advised to verify all information before practical
or commercial use. The journal and its editorial board are not liable for any errors, losses or
consequences arising from its use.
Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
About the Journal
9.17Impact Factor
UGC AlignedSuggested Parameters
TransparentPeer Review
Open AccessNo Paywall
CrossrefDOI Available
Monthly12 Issues / Year
Since 2013Established
2320-2882ISSN
How Publication Works
1Submit Your PaperOnline or by email. A registration ID is issued straight away.
2Transparent Peer ReviewDouble-blind review by subject experts, reported openly.
3AcceptanceThe result reaches you by email and SMS against your registration ID.
4Published Open AccessFree to read worldwide, with certificate and letter ready to download.
Article Impact
39Views
28Downloads
67Total reads
Dec 2025Published
Published · 285 days ago
Today · last read today
2.0 views and 1.4 downloads per day over 20 days counted.
Counts exclude identified crawler traffic and repeat hits from the same reader within 30 minutes. Downloads reached directly through a search engine or an existing citation link are not included, so the figure is a conservative count of activity through this site. Full statistics
Discovery & Indexing
Find this paper on search engines and scholarly indexes, and check the journal’s verified records.
Find this paperSearch
Google ScholarExact-title search, with citing articles once indexed.Search
CrossrefMetadata search across all Crossref records.Search
Semantic ScholarAI-powered literature search.Search- BASEBASEBielefeld Academic Search Engine — open-access harvester.Search
ResearchGateResearch network publication search.Search
Journal recordsVerified
“Search” opens a live search for this paper; “Verified” opens a journal record checked on the Indexing & Discovery page. Third-party services index at their own discretion, often several weeks after publication.
