## 5 Research Methodology and Data Analysis
### The Methodological Justification and Model Selection Criteria  
In formulating the methodological framework for this study, the research prioritized methodologies that balanced predictive accuracy, interpretability, and operational feasibility within aviation industry constraints. The selection criteria emphasized techniques capable of generating reliable forecasts while maintaining compatibility with airline operational protocols, regulatory compliance standards, and decision-making workflows. Three principal modeling approaches were implemented to address distinct aspects of Aircraft On-Ground (AOG) event prediction. Logistic Regression was the foundational model for the binary classification of AOG event occurrence. Its adoption was motivated by parametric transparency, enabling the explicit identification of risk factor coefficients (e.g., aircraft type, maintenance history) and their probabilistic influence on outcomes. This interpretability aligns with aviation safety regulations requiring auditable decision logic for engineering teams and compliance auditors. Temporal forecasting components employed Holt-Winters exponential smoothing and ARIMA models to analyze seasonality and trend patterns in AOG event frequency. These time series approaches were selected for their computational efficiency with moderate-sized datasets and demonstrated effectiveness in capturing cyclical maintenance-related phenomena, such as recurrent component failures associated with seasonal operational demands. The methodological considerations warrant clarification regarding reviewer suggestions concerning advanced computational techniques (e.g., LSTM networks, 5-curve models). While neural architectures theoretically offer enhanced pattern recognition capabilities for complex temporal dependencies, three empirical constraints influenced their exclusion:  
1. Data Limitations: Aviation operational data, governed by stringent privacy regulations (EU 2018/1139, ICAO Annex 6), exhibited restricted temporal granularity and feature continuity. Deep learning implementations typically require extensive training datasets with high-frequency temporal resolution, which regulatory data-sharing protocols preclude. 2. Interpretability Requirements: Aviation safety management systems (SMSs) under EASA Part-CC regulations mandate traceable risk assessment processes. Black-box models, despite potential predictive advantages, introduce accountability challenges during safety case evaluations and regulatory audits. 3. System Integration Constraints: Legacy aviation resource management platforms (e.g., RAIDO, eCentral8) utilize deterministic scheduling algorithms with limited machine learning interoperability. Model complexity thresholds exist to maintain real-time operational responsiveness and minimize computational resource allocation. The Discussion Section acknowledges emerging opportunities for hybrid modeling frameworks as aviation data infrastructures evolve. The potential integration of survival analysis for component lifecycle modeling and federated learning approaches for cross-carrier data collaboration is posited as a future research direction, contingent upon industry-wide improvements in data standardization and secure sharing protocols. Such advancements may gradually mitigate current implementation barriers while preserving essential regulatory safeguards. This methodological paradigm reflects a conscious alignment with current aviation industry capabilities rather than exclusively optimizing theoretical model sophistication. The approach ensures practical deployability while establishing foundational infrastructure for subsequent technological adoption as sector-wide digital transformation initiatives progress. **Quantitative Analysis: AOG Incidents and Statistical Assessment of Flight Delays and Schedule Disruptions**  
This section examines trends in AOG incidents at SmartLynx from 2020 to 2024 [38], (Table 6) exploring how fleet reliability, operational performance, and predictive maintenance strategies are interconnected.