Search Results (283)

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

The aviation industry is under increasing pressure to reduce its environmental impact while maintaining safety and performance standards. One promising area for improvement lies in the use of sustainable materials in airport infrastructure. One of the issues preventing uptake of emerging sustainable technologies is the lack of guidance relating to the opportunities, potential benefits, associated risks and an implementation plan specific to airport pavements. This research reviewed opportunities to incorporate waste materials into rigid airport pavements, focusing on concrete base slabs. Commonly used supplementary cementitious materials (SCMs), such as fly ash and ground granulated blast furnace slag (GGBFS) were considered, as well as recycled aggregates, including recycled concrete aggregate (RCA), recycled crushed glass (RCG), and blast furnace slag (BFS). Environmental Product Declarations (EPDs) were also used to quantify the potential for environmental benefit associated with various concrete mixtures, with findings showing 23% to 50% reductions in embodied carbon are possible for selected theoretical concrete mixtures that incorporate waste materials. With considered evaluation and structured implementation, the integration of waste materials into rigid airport pavements offers a practical and effective route to improve environmental outcomes in aviation infrastructure. It was concluded that a Triple Bottom Line (TBL) framework—assessing financial, environmental, and social factors—guides material selection and can support sustainable decision-making, as does performance-based specifications that enable sustainable technologies to be incorporated into airport pavement. The study also proposed a consequence-based implementation hierarchy to facilitate responsible adoption of waste materials in airside pavements. The outcomes of this review will assist airport managers and pavement designers to implement practical changes to achieve more sustainable rigid airport pavements in the future. Full article

This study focuses on surface wind analysis at the small regional airport in Svidnik, used primarily for pilot training under daytime VFR conditions. Due to the complex local terrain and lack of prior meteorological data, an automatic weather station was installed, collecting over 208,000 wind measurements over a two-year period at ten-minute intervals. The dataset was processed using hierarchical filtering and statistical selection, and visualized via wind rose diagrams. The results confirmed a dominant southeastern wind component, supporting the current runway orientation (01/19). However, a less frequent easterly wind direction was identified as a safety concern, causing turbulence near the runway due to terrain and vegetation. This is particularly critical for trainee pilots during final approach and landing. Statistical analysis showed that easterly winds, though less common, appear year-round with a peak in summer. Pearson correlation and linear regression confirmed a significant relationship between the number of easterly wind days and their measurement frequency. Daytime winds were stronger than nighttime, justifying the focus on daylight data. The study provides practical recommendations for training flight safety and highlights the value of localized wind monitoring at small airports. The presented methodology offers a framework for improving operational awareness and reducing risk in complex environments. Full article

Foreign Object Debris (FOD) detection is paramount for airport operations. The precise identification and removal of FOD are critical for ensuring airplane flight safety. This study collected FOD images using optical imaging sensors installed at Urumqi Airport and created a custom FOD dataset based on these images. To address the challenges of small targets and complex backgrounds in the dataset, this paper proposes optimizations and improvements based on the advanced detection network Dab-Deformable. First, this paper introduces a Lightweight Deep-Shallow Feature Fusion algorithm (LDSFF), which integrates a hotspot sensing network and a spatial mapping enhancer aimed at focusing the model on significant regions. Second, we devise a Multi-Directional Deformable Channel Attention (MDDCA) module for rational feature weight allocation. Furthermore, a feedback mechanism is incorporated into the encoder structure, enhancing the model’s capacity to capture complex dependencies within sequential data. Additionally, when combined with a Threshold Selection (TS) algorithm, the model effectively mitigates the distraction caused by the serialization of multi-layer feature maps in the Transformer architecture. Experimental results on the optical small FOD dataset show that the proposed network achieves a robust performance and improved accuracy in FOD detection. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. Full article

Wildlife strikes in aviation are among the most reported safety incidents. As such, strikes have become the fundamental unit of understanding of the risk posed by wildlife. However, the management of wildlife risks to aviation has shifted to a hazard management philosophy. This literature review examines the argument that current wildlife strike reporting requirements are inadequate for modern wildlife hazard management techniques. This review utilised bibliometric analysis software to identify relevant academic research sourced from the Web of Science, as well as industry materials, to compile a final catalogue (n = 542). Further filtering revealed a limited set of relevant papers (n = 42) and even fewer papers that addressed the above question. Analysis of these papers and the wider catalogue noted limitations in current reporting requirements as they relate to hazard and risk management concepts. This analysis was supplemented with a review of international standards and relevant national requirements, concluding that while academics and industry have adopted systematic safety and hazard management techniques, and international guidance material has kept pace, international standards, the foundation for many national reporting systems, remain decades behind. This paper proposes the use of robust consensus-building methodologies, such as the Delphi technique, in the industry as a means of streamlining and supporting international standards development. Full article

Accurate and efficient object detection is essential for aerial airport surveillance, playing a critical role in aviation safety and the advancement of autonomous operations. Although recent deep learning approaches have achieved notable progress, significant challenges persist, including severe object occlusion, extreme scale variation, dense panoramic clutter, and the detection of very small targets. In this study, we introduce a novel and unified detection framework designed to address these issues comprehensively. Our method integrates a Normalized Gaussian Wasserstein Distance loss for precise probabilistic bounding box regression, Dilation-wise Residual modules for improved multi-scale feature extraction, a Hierarchical Screening Feature Pyramid Network for effective hierarchical feature fusion, and DualConv modules for lightweight yet robust feature representation. Extensive experiments conducted on two public airport surveillance datasets, ASS1 and ASS2, demonstrate that our approach yields substantial improvements in detection accuracy. Specifically, the proposed method achieves an improvement of up to 14.6 percentage points in mean Average Precision (mAP@0.5) compared to state-of-the-art YOLO variants, with particularly notable gains in challenging small-object categories such as personnel detection. These results highlight the effectiveness and practical value of the proposed framework in advancing aviation safety and operational autonomy in airport environments. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues such as flooded runways and the disruption of power supplies highlight the need for strong adaptation strategies. The study focuses on the application of the high-resolution regional model COSMO-CLM to assess climate change impacts on Roma Fiumicino airport (Italy) under the IPCC RCP8.5 scenario. The complex topography of Italy requires fine-scale simulation to catch localized climate dynamics. By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. The findings provide valuable insights for designing resilient airport infrastructures and updating operational protocols in view of evolving climate risks. A consistent increase in daily temperatures is projected, along with a modest positive trend in annual precipitation. The use of advanced statistical methods revealed insights into the fractal dimensions and frequency components of climate variables, showing an increasing complexity and variability of future climatic patterns. Full article

Braking systems are essential to aircraft safety and operational efficiency; however, the variability of carbon brake wear, driven by the intricate interplay of operational and environmental factors, presents challenges for effective maintenance planning. This effort leverages machine learning classifiers to predict wear severity using operational data from an airline’s wide-body fleet equipped with wear pin sensors that measure the percentage of carbon pad remaining on each brake. Aircraft-specific metrics from flight data are augmented with weather and airport parameters from FlightAware^® to better capture the operational environment. Through a systematic benchmarking of multiple classifiers, combined with structured hyperparameter tuning and uncertainty quantification, LGBM and Decision Tree models emerge as top performers, achieving predictive accuracies of up to 98.92%. The inclusion of environmental variables substantially improves model performance, with relative humidity and wind direction identified as key predictors. While machine learning has been extensively applied to predictive maintenance contexts, this work advances the field of brake wear prediction by integrating a comprehensive dataset that incorporates operational, environmental, and airport-specific features. In doing so, it addresses a notable gap in the existing literature regarding the impact of contextual variables on brake wear prediction. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

Because of terrain disruption of the airflow and interface between different airmasses, wave motions may be observed in the vicinity of the Hong Kong International Airport using plan position indicator scans and range height indicator scans with Doppler light detection and ranging systems. This paper documents three cases of wave motion that are not commonly observed near this airport and have never been described before in the literature for Hong Kong, including one mountain wave case and two cases of interfaces between airmasses. These waves may have impacts on aviation safety by leading to the occurrence of low-level windshear and turbulence. They are studied further using a high-resolution numerical weather prediction model. It is found that the model is capable of capturing some features of the waves, such as their occurrence mechanisms. However, some details of the waves are not successfully reproduced, such as the changes in the number of wave crests/troughs with time. Further study should also be conducted to reproduce the wavelengths of these waves. Full article

General aviation trajectory prediction plays a crucial role in enhancing safety and operational efficiency at non-towered airports. However, current research faces multiple challenges including variable weather conditions, complex aircraft interactions, and flight pattern constraints specified by general aviation regulations. This paper proposes a deep learning method based on stochastic processes aimed at addressing uncertainty issues in general aviation trajectory prediction. First, we design a probabilistic encoder–decoder structure enabling the model to output trajectory distributions rather than single paths, with regularization terms based on Lyapunov stability theory to ensure predicted trajectories maintain stable convergence while satisfying flight patterns. Second, we develop a multi-layer attention mechanism that accounts for weather factors, enhancing the model’s responsiveness to environmental changes. Validation using the TrajAir dataset from Pittsburgh-Butler Regional Airport (KBTP) not only advances deep learning applications in general aviation but also provides new insights for solving trajectory prediction problems. Full article

Thunderstorms downbursts can be hazardous during aircraft landing and take-off. A warming climate increases low- to mid-level troposphere water vapor, typically transported from high sea-surface temperature regions. Consequently, the future occurrence and intensity of destructive wind gusts from wet microburst thunderstorms are expected to increase. Wet microbursts are downdrafts from heavily precipitating thunderstorms and are several kilometers in diameter, often producing near-surface extreme wind gusts. Brisbane airport recorded a wet microburst wind gust of 157 km/h in November 2016. Numerous locations in eastern Australia experience warm season (October to March) wet microbursts. Here, eight machine learning techniques comprising forward and backward linear regression, radial basis forward and backward support vector regression, polynomial-based forward and backward support vector regression, and forward and backward random forest selection were employed. They identified primary attributes for increased atmospheric instability by warm moist air influx from regions of high sea-surface temperatures. The climate drivers detected here are indicative of increased future eastern Australian warm season thunderstorm downbursts, occurring as wet microbursts. They suggest a greater frequency and intensity of impacts on aircraft safety and operations affecting major east coast airports, such as Sydney and Brisbane, and smaller aircraft at inland regional airports in southeastern Australia. Full article