Abstract

The Notice to Airmen (NOTAM) system is essential for aviation safety, giving critical information on risks and operating limitations. However, the volume and complexity of NOTAM data complicate interpretation, which could compromise safety and efficiency. This study tries to address these issues by creating a predictive algorithm for analyzing NOTAM data and predicting whether to keep or remove them. The methodology combines effective machine learning algorithms to reveal insights that improve safety and operational efficiency. Using a dataset of NOTAM entries, multiple prediction algorithms are tested to forecast possible problems and enable proactive risk management. The study's findings help to improve the effectiveness and dependability of NOTAM systems, resulting in increased aviation safety and smooth air traffic management. The proposed model for this study is Support Vector Machine model with the TF-IDF transformed data, the classification performance was assessed using standard evaluation measures such as accuracy, precision, recall, F1-score and ROC. The results showed that the SVM model was 76% accurate in categorizing NOTAM entries as "keep" or "remove."

Library of Congress Subject Headings

Aeronautics--Safety measures; Machine learning; Support vector machines

Publication Date

5-11-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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