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One of the inputs to a flight planning system is human-generated Notice to Airmen (NOTAM) entries that are used to alert flight crews of potential hazards that may be encountered when flying a specific mission. The text descriptions contained within the NOTAM are heavily abbreviated domain-specific free text, not standardized, and can vary widely depending on how the issuer chooses to describe the situation. Automated flight planning systems (autoplanners) do not parse the text description to determine accuracy or viability. Instead, a 4-letter code (known as a Q-Code) is entered and recorded by the human issuer to aid in interpreting the NOTAM contents. If Q-Codes are improperly entered, wrong, incomplete, or not specific to the issue being described, NOTAMs may be erroneously interpreted and the autoplanner may generate sub-optimal flight plans. As a solution to the problem, we developed machine-learning based text classification models where the inputs are NOTAM text descriptions and the outputs are predicted Q-codes for each text description. Such a solution would make autoplanner systems more robust by verifying and correcting incorrect NOTAMs automatically.
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