## 1 Introduction  
Due to deviations between planned and actual operations, airports are often forced to make tactical adjustments to their resource allocation plans. This challenge is particularly acute during peak operations, when buffer capacity is rarely available. At present, airports have limited means of projecting pre-tactical and tactical block time deviations (gate slots) for both short- and long-haul routes, which would be invaluable for optimizing planning. This is primarily because the greatest visibility into flight progress typically occurs only once a flight plan is activated, which, in the European region, takes place three hours prior to the estimated off-block time. According to [PERSON] [1] and [PERSON] [2], flight delays adversely affect the economy and operations for all parties involved--namely, airlines, passengers, and the network, which encompasses airports. Therefore, any reduction in these consequences can improve both the efficiency and effectiveness of air travel. The path forward involves improving the predictability of flights, especially those arriving from beyond the Air Traffic Flow and Capacity Management (ATFCM) area. This can be achieved by reinforcing the principles of pre-tactical and tactical planning, in line with the concept of Total Airport Management, supported by an Airport Operations Centre and Airport Operations Plan [3]. Flight delays arise due to a range of factors, including adverse weather conditions [4], congestion at origin or destination airports, airspace limitations, mechanical issues with aircraft, and inefficiencies in airline scheduling [5]. These delays can cascade through the network, propagating to subsequent flights and exacerbating overall operational disruptions [6]. Additionally, unexpected events such as diversions can also contribute to delays [7], although such scenarios are less frequently analyzed within the scope of flight delay studies. A significant body of research has focused on studying flight predictability and delays, with the root causes of delays being key subjects of interest to support operational planning and management for air traffic control, airlines, airport operators, ground handlers, and the Network Manager (NM). Various prediction techniques have been explored by authors such as [PERSON], [PERSON], and [PERSON] [8; 9; 10], who utilized statistical methods. The probabilistic methods were further applied in studies by [PERSON], [PERSON], and [PERSON] [11; 12; 13]. Network-based methods were explored by [PERSON], [PERSON], and [PERSON] [14; 15; 16], while operational methods were investigated by [PERSON] and [PERSON] [17; 18]. Machine learning approaches have also been used, as seen in the work of [PERSON], [PERSON], [PERSON], and [PERSON] [19; 20; 21; 22], where models were designed to predict flight delays. Part of these machine learning studies, such as [23], developed deep learning models and supervised learning models such as decision trees and random forests to predict air traffic delays. According to [PERSON] [6], delays can propagate and affect subsequent flights at both departure and arrival airports. Thus, improving the ability to predict delays and control the factors that contribute to them remains a crucial objective. Their study employed a Support Vector Machine model to explore the non-linear relationship between flight delay outcomes. A particularly notable study by [PERSON] [24], using supervised machine learning and data mining techniques, addressed the need to improve the predictability of actual landing times. The authors rightly identified that historical data could help predict delays on arrival. Their predictive modeling, based on a Multi-Linear Regression (MLR) model, predicted arrival variations using information available at the time of departure. Their study also revealed key factors responsible for early arrivals, with flight length being one of the primary elements in the MLR equation. Research has also focused on evaluating the impact of delays on the turnaround process.