Authors - Mohd. Zuhaib Ahmed, Akash Priya, Deepti Chopra, Pankaj Kumar Abstract - Effective landing and take-off (LTO) decision-making in mil itary aviation is critically dependent on airfield serviceability and pre vailing weather conditions. A fundamental challenge is the absence of structured expert pilot decision logs, as such data are operationally sen sitive and access-restricted. This work presents a replicable methodolog ical framework for developing machine learning-based decision support systems in domains where operational data are scarce or classified. The pipeline encompasses synthetic data forged using correlated Monte Carlo sampling, constrained by location-specific geographic, seasonal, and ter rain parameters across ten Indian Air Force (IAF) stations, yielding ap proximately 60,000 simulated operational scenarios. The dataset is gen erated within domain-constrained operational bounds to ensure physi cal plausibility. A rule-based expert classification system assigns opera tional status as Green (Safe), Orange (Caution), or Red (Unsafe); four ML algorithms are subsequently evaluated: Logistic Regression, Naïve Bayes, Support Vector Machines, and Decision Trees. The Decision Tree achieves the highest performance, with an accuracy of 0.983, an F1 score of 0.983, and a ROC-AUC of 0.984. The proposed framework supports two deployment pathways: the rule engine as a deterministic automa tion tool for standard clearances, and the ML model as the inference core of a real-time Human-in-Loop (HIL) expert system requiring opera tor authorisation at every decision. As expert pilot decision logs become available, the system may be progressively elevated to a fully adaptive expert system.
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