Flight safety remains a central concern in civil aviation. Recently, increasing attention has been given to leveraging high-dimensional temporal flight data, typically collected by Quick Access Recorders (QAR), to predict safety events. However, most existing studies focus on individual events, overlooking the latent correlations among multiple events. For instance, during landing, a maneuver that reduces the risk of one event may inadvertently increase the risk of another. Predicting multiple events introduces three main challenges: 1)handling parameters recorded at inconsistent sampling rates, 2) learning task-specific parameter importance for interpretability, and 3) modeling complex temporal dependencies for accuracy. To address these, we propose MultiSafe, a deep multi-task learning model for predicting and interpreting multiple flight safety events. First, to process parameters with heterogeneous frequencies, we introduce a Multi-Scale Shared Encoder with adaptive convolutional kernels to unify representations across tasks. Next, a gating-based Parameter Selector learns task-specific parameter importance, enabling interpretable predictions. Finally, a Temporal Decoder with fine-grained attention captures intricate temporal dependencies among parameters. Experiments on a dataset of 37,518 A320 flight records demonstrate that MultiSafe outperforms state-of-the-art baselines in prediction accuracy, while its interpretability offers actionable insights to assist pilots in enhancing flight safety.

Graph neural network (GNN)-based approaches have achieved remarkable success in temporal knowledge graph (TKG) reasoning. Despite these advances, two critical challenges remain: 1) inadequate modeling of local contextual dynamics, which limits the adaptability of entity and relation representations to specific queries and 2) inadequate mechanisms for handling emerging patterns, that is, novel interactions absent from historical data, which reduces predictive performance in dynamic environments. To address these limitations, we propose TCDR-PD, a temporal and contextual dynamic representation network with pattern decomposition. TCDR-PD introduces a temporal and contextual dynamic representation learning (TCDR) module to capture both global temporal trends and query-specific contextual dynamics, enabling more precise embeddings. Additionally, the pattern decomposition (PD) prediction module explicitly disentangles the prediction of recurring and emerging patterns, enabling tailored strategies to improve reasoning performance. Experiments on four benchmark datasets demonstrate that TCDR-PD outperforms state-of-the-art methods, effectively supporting stable reasoning over evolving TKGs.

Hard landings, as a common type of aviation incident, have consistently attracted the attention of airlines and aviation authorities. In recent years, the widespread adoption of Quick Access Recorder (QAR) systems has led numerous researchers to focus on predicting hard landing events through the analysis of QAR data. However, most studies treat QAR data as standard time series without fully accounting for its unique characteristics. Unlike typical time series, QAR data exhibits limited periodicity and trends, making it challenging for traditional modeling approaches to capture its complex patterns. Furthermore, model interpretability, as an essential aspect for practical deployment and decision-making, remains insufficiently explored. To address these issues, we propose a Fine-Grained Time and Hidden Feature Learning model for Interpretable Hard Landing Prediction based on QAR Data (TF-QAR). Specifically, we introduce a novel fine-grained temporal aggregation module, which dynamically extracts the importance of each time step through learnable parameters, to efficiently model the temporal dependencies in QAR data. Additionally, we develop a feature aggregation module that introduces a learnable adjacency matrix to model the significance of flight features and their interrelationships, revealing not only key parameters that directly influence hard landings, but also hidden parameters that are indirectly related. We conducted extensive experiments using a dataset of 37,929 real A320 flight segments in China. The results demonstrate that our model outperforms existing state-of-the-art baselines. Moreover, by visualizing the learnable parameters, TF-QAR provides interpretable insights valuable for pilot decision-making, offering practical support for the prevention and management of hard landing events.

Flight safety incidents, such as hard landings and tail strike risks, represent critical concerns during the landing phase. Although Quick Access Recorder (QAR) systems collect extensive multivariate flight data, previous studies have faced challenges in effectively modeling the complex interdependencies between flight parameters, which has limited their ability to predict multiple safety incidents simultaneously. To address this issue, we propose a novel model, named MDGNN, to capture hidden spatio-temporal dependencies and predict both hard landing and tail strike risk incidents. Specifically, we employ temporal convolutional networks (TCNs) to extract both localized representations and long-term temporal trends from multivariate flight data, ensuring the standardization of flight parameters across varying frequencies. Additionally, we are the first to construct a dynamic graph to model temporal relationships, applying a dynamic graph neural network and a temporal convolution module to accurately capture intricate spatial and temporal dependencies. Extensive experiments conducted on 37,904 Airbus A320 flight samples demonstrate that the MDGNN model surpasses state-of-the-art baselines with high prediction accuracy. Furthermore, a case study visualizing key flight parameters highlights the model’s ability to reveal the root causes of safety exceedances, offering valuable insights for flight safety analysis.

Robustness is paramount for ensuring the reliability of knowledge graph models in safety-sensitive applications. While recent research has delved into adversarial attacks on static knowledge graph models, the exploration of more practical temporal knowledge graphs has been largely overlooked. To fill this gap, we present the Adaptive Temporal Perturbation Framework (ATPF), a novel adversarial attack framework aimed at probing the robustness of temporal knowledge graph (TKG) models. The general idea of ATPF is to inject perturbations into the victim model input to undermine the prediction. First, we propose the Temporal Perturbation Prioritization (TPP) algorithm, which identifies the optimal time sequence for perturbation injection before initiating attacks. Subsequently, we design the Rank-Based Edge Manipulation (RBEM) algorithm, enabling the generation of both edge addition and removal perturbations under black-box setting. With ATPF, we present two adversarial attack methods: the stringent ATPF-hard and the more lenient ATPF-soft, each imposing different perturbation constraints. Our experimental evaluations on the link prediction task for TKGs demonstrate the superior attack performance of our methods compared to baseline methods. Furthermore, we find that strategically placing a single perturbation often suffices to successfully compromise a target link.

Flight safety is a key issue in the aviation industry. Recently, with the prevalence of flight data recording systems, some deep learning-based studies have been devoted to predicting safety incidents based on flight data. However, these studies, although they exhibit higher prediction accuracy, have largely neglected the interpretability analysis of safety incidents which is of great concern to airlines and pilots. To address this issue, we define flight safety prediction as a multiscale time series classification problem and propose an interpretable model named IMTCN to provide both accurate predictions and high interpretability of flight safety. First, multiple temporal convolutional networks (TCNs) are utilized to capture local representations and long effective histories from multivariate flight data. Because different flight parameters are collected with diverse sampling frequencies, multiple TCNs are used to handle these parameters separately. Then, we creatively adapt the class activation mapping (CAM) method, which has been used for interpretation in image classification, and combine it with the TCN to provide flight data interpretability. The established model can pinpoint key flight parameters and corresponding moments that contribute most to safety incidents. Experimental results on a real-world dataset with 37,943 Airbus A320 aircraft flights show that our model outperforms the baselines on the task of exceedance classification and prediction 2 seconds and 4 seconds in advance, and case studies demonstrate its superb interpretability for flight safety analysis.

Hard landing, as one of the most frequent flight safety incidents during the landing stage, is highly concerned by the aviation industry. Recently, the popularization of Quick Access Recorder (QAR), a modern flight data recording system, has made it possible to collect large volume of flight parameters and incorporate state-of-the-art AI technologies to improve flight safety. However, due to the complex, multivariate, and highly specialized nature of QAR data, most existing studies either suffer from information loss caused by rough feature extraction methods, or rely solely on black-box models with no interpretations, making themselves difficult to achieve satisfactory performance in terms of prediction and explainability. To address this issue, we propose a novel attention-driven model named SDTAN (Scalable Deep Time-Aware Attention Network), which can accurately predict hard landing events and provide interpretable insights to help reveal the possible reasons leading to the events. Specifically, SDTAN fully captures information to learn the local representations of parameters, and leverages the time-interval attention mechanism to focus on the entire temporal pattern of flight over the relevant time intervals. It further re-encodes the representations of parameters in a global view and learns the global effect of parameters on the predicted output to uncover the ones which strongly indicate the flight safety status, enabling both high prediction accuracy and qualitative interpretability. We conduct experiments on real-world QAR datasets of 37,920 Airbus A320 flight samples. Experimental results demonstrate that SDTAN outperforms other state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters.

Hard landing is a typical flight safety incident, and interpretability plays an important role in flight safety research. However, existing studies failed to provide good interpretability of the reasons for hard landing incidents and suffer from low prediction accuracy. To address the above problems, in this paper we propose CurveCluster+, a curve clustering method based on quick access recorder (QAR) data for hard landing risk evaluation. Specifically, we first conduct an in-depth analysis on hard landing flights by comparing key QAR parameter curves with the group behavior, based on which we establish a two-level hierarchical classification of hard landing incidents according to the hard landing patterns. Then we extract curve-level features from key QAR parameters through interpolation and resampling. After that we turn the classic K-means clustering into a semi-supervised algorithm by incorporating some expert experience and apply it on the curve-level features to automatically recognize the hard landing patterns. Finally, we propose a risk evaluation model based on the clustering results to discover high-risk flights from normal ones. We evaluate our method on a QAR dataset of 37,943 Airbus 320 aircraft flights. The results show that compared with other state-of-the-art data-driven methods, CurveCluster+ provides strong interpretability of hard landing incidents and exhibits good performance in recognizing hard landing patterns (the overall accuracy of our method reaches up to 92.99%). Moreover, it only requires a handful of hard landing samples to discover high-risk flights from tremendous normal landing flights, which is critical for flight safety warnings.

Relational triples are the essential parts of knowledge graphs, which can be usually found in natural language sentences. Relational triple extraction aims to extract all entity pairs with semantic relations from sentences. Recent studies on triple extraction focus on the triple overlap problem where multiple relational triples share single entities or entity pairs in a sentence. Besides, we find sentences may contain implicit relations, and it is challenging for most existing methods to extract implicit relational triples whose relations are implicit in the sentence. In this paper, we propose a relation-guided attention mechanism (RGAM) for relational triple extraction. Firstly, we extract subjects of all possible triples from the sentence, and then identify the corresponding objects under target relations with relation guidance. We utilize relations as prior knowledge instead of regarding relations as classification labels, and apply attention mechanism to obtain fine-grained relation representations, which guide extracted subjects to find the corresponding objects. Our approach (RGAM) can not only learn multiple dependencies in each triple, but also be suitable for extracting implicit relational triples and handling the overlapping triple problem. Extensive experiments show that our model achieves state-of-the-art performance on two public datasets NYT and WebNLG, which demonstrates the effectiveness of our approach.