Decision-Making Model for Aircraft Landing Based on Fuzzy Logic Approach
Bibliographic record
Abstract
An aircraft's landing stage involves inherent hazards and problems associated with many factors, such as weather, runway conditions, pilot experiences, etc.The pilot is responsible for selecting the proper landing procedure based on information provided by the landing console operator (LCO).Given the likelihood of human decisions due to errors and biases, creating an intelligent system becomes important to predict accurate decisions.This paper proposes the fuzzy logic method, which intends to handle the uncertainty and ambiguity inherent in the landing phase, providing intelligent decision support to the pilot while reducing the workload of the LCO.The fuzzy system, built using the Mamdani approach in MATLAB software, considers critical inputs like wind speed, wind direction, visibility, and runway condition to determine the landing's feasibility.The connection between the fuzzy rules is shown in the plotted curves, which indicate the smoothness and absence of overlap of decision-making rules for various input scenarios.A study employing data from Baghdad International Airport found that the proposed fuzzy approach predicted landing feasibility with an outstanding more than 85% accuracy across 20 different real-world scenarios.This level of reliability demonstrates how well the system can assess varied weather and runway conditions and identify the best landing decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".