Infrasound are permanently recorded by the International Monitoring System (IMS) set up to detect one kiloton equivalent nuclear explosion around the world (Marty et al. 2019 [2]) and monitor the compliance of the Comprehensive Nuclear T est-Ban-T reaty (CTBT). Accurate modeling of infrasound transmission loss (TL) is essential to interpret microbarometer measurements, evaluate their detection thresholds and characterise wavefield parameters (direction of arrival, velocities, amplitudes, frequencies) and source informations (ground pressure levels associated to earthquakes, acoustic energy from man-made or volcanic explosions). TLs modeling can also help to better characterise the middle atmosphere (MA, 15 100 km) which significantly impact the infrasound propagation. The computational cost of existing numerical propagation modeling tools, such as normal modes or full-waveform simulations (parabolic equations, PEs, Waxler et al. 2021 [3]), does not currently allow the exploration of a wide parameter space (variations in atmospheric states, representation of small-scale variability, frequency and source location) for near-real time TLs predictions; making them unusable within the required CTBT operational framework. Reducing these computation times by neglecting part of the complexity of the propagation phenomenon introduces significant uncertainties in predicted TLs. For example, Le Pichon et al. 2012 [4] proposed an approach relying on heuristic modelling of wave attenuation using a semi-analytical formula mapping wind speeds in the MA to TLs at ground level. However, this method has been optimized on idealized atmospheric models neglecting range-dependent variations in the atmosphere, resulting in large errors for unfavorable initial wind conditions. Artificial intelligence methods are currently explored by Brissaud et al. 2023 [1] in the Norwegian Seismic Array (NORSAR

Using data sources beyond the Automatic Identification System to represent the context a vessel is navigating in and consequently improve situation awareness is still rare in machine learning approaches to vessel trajectory prediction (VTP). In inland shipping, where vessel movement is constrained within fairways, navigational context information is indispensable. In this contribution targeting inland VTP, Gaussian Mixture Models (GMMs) are applied, on a fused dataset of AIS and discharge measurements, to generate multi-modal distribution curves, capturing typical lateral vessel positioning in the fairway and dislocation speeds along the waterway. By sampling the probability density curves of the GMMs, feature vectors are derived which are used, together with spatio-temporal vessel features and fairway geometries, as input to a VTP transformer model. The incorporation of these distribution features of both the current and forthcoming navigation context improves prediction accuracy. The superiority of the model over a previously proposed transformer model for inland VTP is shown. The novelty lies in the provision of preprocessed, statistics-based features representing the conditioned spatial context, rather than relying on the model to extract relevant features for the VTP task from contextual data. Oversimplification of the complexity of inland navigation patterns by assuming a single typical route or selecting specific clusters prior to model application is avoided by giving the model access to the entire distribution information. The methodology's generalizability is demonstrated through the usage of data of 3 distinct river sections. It can be integrated into an interaction-aware prediction framework, where insights into the positioning of the actual vessel behavior in the overall distribution at the current location and discharge can enhance trajectory prediction accuracy.

Physics-related and model-based vessel trajectory prediction is highly accurate but requires specific knowledge of the vessel under consideration which is not always practical. Machine learning-based trajectory prediction models do not require expert knowledge, but rely on the implicit knowledge extracted from massive amounts of data. Several deep learning (DL) methods for vessel trajectory prediction have recently been suggested. The DL models developed typically only process information about the (dis)location of vessels defined with respect to a global reference system. In the context of inland navigation, this can be problematic, since without knowledge of the limited navigable space, irrealistic trajectories are likely to be determined. If spatial constraintes are introduced, e.g., by implementing an additional submodule to process map data, however, overall complexity increases. Instead of processing the vessel displacement information on the one hand and the spatial information on the other hand, the paper proposes the merging of both information. Here, fairway-related and navigation-related displacement information are used directly. In this way, the previously proposed context-sensitive Classification Transformer (CSCT) shows an improved spatial awareness. Additionally, the CSCT is adapted to assess the model uncertainty by enabling dropout during inference. This approach is trained on different inland waterways to analyze its generalizability. As the improved CSCT obtains lower prediction errors and enables to estimate the trustworthiness of each prediction, it is more suitable for safety-critical applications in inland navigation than previously developed models.