Seabed backscatter data acquired by the multibeam echosounder (MBES) have been identified as a valuable indicator of sediment properties and benthic community characteristics. However, developing robust change detection models with MBES backscatter remains challenging due to the high costs and limited spatial coverage of seabed ground truth data. Lack of absolute backscatter calibration also hinders the comparison between repeated MBES measurements. To mitigate these issues, we propose an unsupervised method to detect seabed changes by fitting a Gaussian Mixture Model to the backscatter difference between two datasets. A relative calibration is conducted based on a stable reference area to eliminate the impact of possible drifts in echosounder characteristics on the backscatter difference. We then model the unchanged class as a zero-mean Gaussian distribution, with its variance constrained by the backscatter uncertainty estimated from the reference area. By processing each incident angle individually, the angular range with the greatest ability for seabed change detection can also be investigated. We demonstrate the effectiveness of the proposed method through two case studies in the Dutch North Sea. The detected changes reveal seasonal and temporal variations in benthic communities, such as sand mason worms, and are consistent with the sediment movement in one of the study areas. This research highlights the value of MBES backscatter data for seabed change detection and provides a cost-effective solution for seabed habitat monitoring with acoustic measurements.

The increase in flight volumes in the aviation industry has significant socioeconomic implications that affect different aspects of our communities and economies. Although it has great economic benefits, it also causes annoyance and disturbance to communities living near airports. The latter requires understanding and prediction of the varying noise levels generated by various aircraft types. Noise assessment on a fleet level is traditionally achieved by using prediction models such as the DOC29. Such models need to be validated using real measurements. For Amsterdam Schiphol Airport, the so-called NOMOS (Noise Monitoring System) with 39 measurement stations is used for this purpose. We analyze the time series of these stations, collecting annual data for the period from 2006 to 2023. The main objective is to determine how the aircraft-generated noise at these stations can be assigned to 13 different aircraft types, taking into account the different noise levels produced by each aircraft type. This is performed by time series analysis of individual stations and the averaged time series over all stations. The results from two least-squares methods, namely unconstrained least squares (LS) and a proposed bounded least squares subject to weighted constraints (BLS + WC), are compared. The constraints are based on certification data as prior information in the least squares method, which is expected to enhance the model's performance. Based on the above two least squares methods, predictions are performed for 2022 and 2023. The results clearly demonstrate the superiority of the BLS + WC over the LS method. We further extend our analysis to predict noise levels for a hypothetical future year with more newer aircraft models. The results indicate a substantial reduction in the noise level compared to 2023. These findings can thus underscore the effectiveness of the proposed method in outperforming the LS and highlight the model's capability to forecast the impact of fleet modernization on noise reduction.

For models that evaluate aircraft noise, thrust is an essential input. From aircraft flight recorder data or measured noise spectra, the engine's rotational speed can be estimated for which a conversion is then needed to obtain the engine's thrust. This research investigates three conversion methods. The first uses the expressions from the ANP database while the second method is based on the fuel flow. The third employs Gas Turbine Simulation Program (GSP) predictions. The thrust estimates are compared to airline performance calculations where significant variations up to 3 dBA in predicted noise were found. Methods one and three were found to be in good agreement with the performance data. An important finding of this paper is that combining methods one and three using least-squares is capable of providing the required conversion expressions, in line with those in the ANP database, but without being limited to a few aircraft types only.

Accurately predicting Earth’s rotation rate, as represented by Length of Day (LOD) variations, is essential for applications such as satellite navigation, climate studies, geophysical research, and disaster prevention. However, predicting LOD is challenging due to its sensitivity to various geophysical and meteorological factors. Current methods, including statistical approaches, often struggle with short-term forecasting accuracy. In this study, we use Monte Carlo Singular Spectrum Analysis (MCSSA) to distinguish between deterministic and non-deterministic components within the LOD time series. The deterministic components are extended using the SSA prediction algorithm. To enhance robustness, we refine Allen and Smith’s methodology (testing significance of eigenmodes against an autoregressive (AR) (1) noise null hypothesis) by integrating an autoregressive moving average (ARMA) model to account for noise, providing valuable insights into the non-deterministic behaviors present in the series. We comprehensively evaluate our methodology through a comparative analysis. For long-term prediction (365 days), we compare our method against the combined LS and autoregressive (AR) method. For short-term prediction (next 10 days), we compare it against the results of the second Earth Orientation Parameters Prediction Comparison Campaign (second EOP PCC). Using the IERS 20 C04 time series, our hybrid model demonstrates a superior long-term prediction accuracy with a mean absolute error (MAE) of 0.201 ms/day on the 365th day. Additionally, the short-term prediction performance is comparable to the second EOP PCC results. These results illustrate that the proposed method efficiently predicts LOD, showing significant improvement in long-term accuracy and robustness in short-term forecasting.

Permanent GNSS stations continuously monitor Earth's crust movements in horizontal and vertical directions. The recorded data include deterministic variations, including linear trends, periodic signals, and offsets, alongside stochastic variations represented by various noise models. Accurately detecting deterministic behaviors depends on a realistic estimation of the observation noise model. A new multivariate algorithm based on Monte Carlo singular spectrum analysis (MCSSA) is developed to analyze the multiple channels of time-series data simultaneously (e.g., different position components or data from multiple stations), considering noise correlations without being limited to a specific noise model. Testing on simulated GNSS data showed that, by increasing the number of channels, the algorithm could accurately identify dominant annual and semiannual components in the presence of colored noise. The results also indicated that unrealistic assumptions about the GNSS position time-series noise model can be misleading in the MCSSA hypothesis testing. Applying the algorithm to real Greenland GNSS data confirmed the significance of annual and semiannual harmonic patterns when white plus flicker noise (FLWN) combinations were considered as the stochastic behavior of data. In the univariate analysis of the vertical position time series contaminated with random walk noise, none of the annual and semiannual signals were interpreted to be significant. At the same time, the proposed multivariate algorithm successfully identified the annual signal but lacked sufficient channels (stations) to confirm the significance of the semiannual signal in the presence of random walk noise. The multivariate analysis has confirmed the significance of semiannual signal across 21 time series contaminated with FLWN, which univariate analysis missed due to a high level of colored noise.

Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .

Identifying the correct stochastic model in GNSS time series is essential to study geophysical parameters such as site velocities, and hence enhancing their accuracy. The rate uncertainty is a critical aspect in GNSS time series analysis. The variance component estimation (VCE) methods commonly utilize unconstrained estimation principles. Simulating 1000-time series for 4 different noise combinations with 10 years’ time span, we have investigated the performance of non-negative least squares VCE (NNLS-VCE) method for identifying an appropriate noise model. Our results are provided for both univariate and multivariate analysis. As the noise model's complexity increases, the significance of employing multivariate analysis is prominent in contrast to univariate analysis. After thorough analysis, we have determined that treating the false-positive model as a stochastic model in time series yields significant insights. Specifically, if the accumulative spectral index is lower than the true value, it results in an underestimation of the rate uncertainty. Conversely, if the index is higher than the actual value, it leads to an overestimation. Additionally, we observed that as the noise model complexity increases, the number of false-positive models also increases. However, the implementation of multivariate analysis mitigates this increase, offering a more realistic and reliable approach. In case of four distinct noise models, the detection power percentages of 98.5%, 90.5%, 69.5%, 29.3% of univariate analysis increased to 99.5%, 99.8%, 88.4% and 83.7% for multivariate analysis.

Over the last decade, there has been a marked increase in the use of drones for various applications including emergency and natural disaster response, payload delivery, aerial imaging and surveillance. There are currently many private and public initiatives that aim to further increase the number of drones and diversify their tasks, offering many associated benefits such as reduction in emissions by replacing traditional and more polluting options with electric unmanned aerial vehicles (UAVs). However, several challenges have to be addressed before a broader implementation is accomplished. One of such challenges is the reported noise annoyance produced by UAVs. This study focuses on the development of data-driven models to predict the dynamics of noise metrics during real UAV operations. Extensive outdoor experimental campaigns were conducted, where array-based measurements were recorded during several manoeuvres performed by different types of drones. Beamforming techniques were applied to improve data quality and signal-tonoise ratio (SNR), and to synchronize telemetry and acoustics data streams. Using the improved experimental data, an initial machine learning model was developed to predict the backpropagated OSP L as function of telemetry-derived operational parameters for ascent, hover, and descent. The model managed to accurately predict the experimental data, and it was found that the elevation angle was the most important predictor of OSP L for the considered manoeuvres.

Understanding acoustic characteristics of aircraft is critical for designing optimal fleet compositions in terms of noise and improved airport operations. This study investigates acoustic signatures across different aircraft types, engine designs, and operational conditions. A dataset consisting of 457 field acoustic measurements of commercial turbofan aircraft landing and taking-off from Amsterdam Airport Schiphol was used. To unveil meaningful patterns, we focused on dimensionality reduction techniques—Principal Component Analysis (PCA) and tdistributed Stochastic Neighbour Embedding (t-SNE)— to analyse this high-dimensional acoustic data. These methods are complemented by clustering algorithms and supervised machine learning models, such as K-Means, random forests for feature importance, and multilayer perceptrons (MLP) to classify aircraft types, engine configurations, and operations. Results reveal a strong loudness axis in the first principal component, overshadowing subtle spectral and timebased differences across aircraft families, especially for takeoffs. Nonetheless, focusing on higher-order components and alternative embeddings (t-SNE) highlights additional spectral and temporal markers. Operation classification (landing vs. takeoff) achieves 98% accuracy, but aircraft and engine family classification remain challenging, with accuracy capped below 50% using these feature sets. These findings suggest that advanced feature selection and dimensionality reduction while considering amplitude characteristics are essential for disentangling nuanced design-based acoustic traits.

Indoor positioning systems based on Bluetooth technology have gained significant attention with the widespread adoption of Bluetooth Low Energy (BLE). The rapid growth of BLE-enabled devices—now exceeding eight billion worldwide—has enabled the development of cost-effective, location-aware applications. The precision of BLE indoor positioning systems depends on several factors, among which the quality of received signal strength indicator (RSSI) measurements and the spatial deployment of sensors are most critical. Existing research has largely focused on improving RSSI accuracy through techniques such as multichannel measurements, outlier detection, Kalman filtering, and regression modeling. In this work, we examine two primary components that govern positioning precision: 1) the uncertainty in the RSSI-distance model parameters, including the path-loss exponent, reference power, and raw RSSI values; and 2) the geometry of sensor deployment, which directly affects estimation precision through the geometric dilution of precision (GDoP). We demonstrate that optimizing sensor placement using centroidal Voronoi tessellation (CVT) reduces GDoP and substantially improves positioning precision. Comparative experiments across two deployment scenarios, one based on CVT, confirm that CVT-based sensor configurations yield significantly higher precision in BLE RSSI-based indoor positioning.

Purpose – This study aims to automate the visual inspection of piling sheets in water channel construction using artificial intelligence (AI). By employing image classification and object detection techniques, the research focuses on extracting and analysing geometric features to enhance the accuracy and efficiency of the inspection process. It also addresses key challenges associated with the unique characteristics of construction materials and the limited variability of available inspection datasets. Design/methodology/approach – Convolutional neural networks (CNNs) with varying complexities are employed for image classification, across four and six classes, and for object detection of piling sheets in water channel environments. A dataset provided by Witteveen + Bos is preprocessed to generate training sets, and the CNN architectures are optimized for enhanced performance. The accuracy and efficiency of the proposed models are evaluated and compared against traditional manual inspection methods. Findings – The AI-driven approach significantly reduces processing time, evaluating 40, 000 images in just 11.9 h, compared to approximately one month using manual assessment. The 4-class classification model achieves an accuracy of 96%, while the 6-class model attains 72%. The object detection model produces a mean average precision (mAP) of 79%. These results meet the performance standards set by the Dutch company Witteveen + Bos, which demonstrate the effectiveness of AI in automating the inspection of piling sheets. Originality/value – This study introduces a novel AI-based approach for assessing piling sheets, demonstrating substantial improvements over traditional inspection methods. It introduces a systematic evaluation of various CNN architectures and hyperparameters to optimize the models specifically for piling sheet inspection rather than relying on off-the-shelf solutions. The use of CNNs for both image classification and object detection adheres to relevant Dutch engineering standards. Notably, the reduction in processing time, from one month to around 12 h, represents a major advancement in the efficiency of civil engineering inspections.

To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. This research evaluates the noise–power–distance (NPD) tables employed in the European Doc 29 noise model using the noise measurements taken around Amsterdam Airport Schiphol. Thrust estimation is based on extracting the blade passing frequency from acoustic measurements and converting it to the engine rotational speed indicator N1%. The N1% estimates are validated with onboard flight data. Even with accurate input parameters (thrust and distance to the observer), discrepancies are observed between modelled and measured noise levels, which can be attributed to the inaccuracies in the NPD tables. To further investigate this, empirical thrust-noise relations are derived from the measurements. These derived relations are found to differ from those in the original NPD tables. When the empirical thrust-noise relations are used, the agreement between the modelled and measured mean noise levels improves. The standard deviation of the differences gets reduced by 25% for departure operations. This finding is subsequently confirmed using independent measurements around Oslo Airport Gardermoen. Beyond improving current best-practice noise modelling, the methodology presented in this research offers insight into the development and validation of NPD tables.

The multibeam echosounder (MBES) has been widely used in seabed mapping, considering its ability to collect continuous and broad-scale seabed measurements efficiently. The presence of shellfish or dead shell material can alter the geophysical properties of the sediment and thus affect the MBES backscatter intensity, making acoustic surveys with the MBES a potential non-invasive solution for regularly monitoring the benthic habitats of shellfish aggregations. Although there exists an increasing interest in mapping marine benthos with MBES measurements recently, the use of multi-spectral backscatter data is still limited. Thus, this research aims to enhance the acoustic mapping of benthic habitats using multi-spectral MBES data, with a focus on a shell bed region in the Dutch North Sea. With backscatter measurements from three frequencies, 90, 300, and 450 kHz, we achieved seabed classification in two steps. First, a semi-supervised backscatter completion was conducted to generate full-coverage backscatter data for each incident angle, mitigating the limited overlap between adjacent survey lines. We then classified the multi-Angle backscatter data from each individual frequency using the Gaussian Mixture Model. Our results indicate an improved seabed classification performance compared to the classical Bayesian method. Comparisons of classification maps across frequencies also show their different abilities to distinguish the shell bed region from other coarse sediments, demonstrating the value of leveraging multi-spectral backscatter data in seabed habitat mapping.

Earth orientation parameters (EOP) are critical for applications in orbit determination, astronomy, space geodesy, and geophysics. Accurate predictions of EOP rely on the identification of both deterministic periodic patterns and noise characteristics. This study addresses these requirements by analyzing the Polar Motion (PM) and Length Of Day (LOD) time series to determine its stochastic model structure, estimated using least squares variance component estimation (LS-VCE). With this model, deterministic periodic patterns were extracted through least squares harmonic estimation (LS-HE) and validated against colored noise components to identify significant signals. Using the IERS 14 C04 data from January 1, 2000, to December 31, 2019, the study identified the noise as power-law with a spectral index of −1.5, suggesting non-stationary fractional Brownian characteristics. LS-HE detected dominant frequencies in PM–notably the Chandler and annual signals–and in LOD, with annual, semiannual, 14-day, and 9-day signals. Building on these findings, short-, mid-, and long-term prediction model were developed for the PM and LOD time series from September 2021 to December 2022. The predictive model combines the LS-HE-extracted signals and the noise model to generate forecasts. These predictions were compared with other models from the Second Earth Orientation Parameter Prediction Comparison Campaign, demonstrating competitive accuracy, particularly for the initial forecast days. The results validate that combining LS-HE with a realistic noise model provides an effective approach for short-term of the PM and LOD forecasting, meeting the accuracy goals of geodetic and geophysical applications.

The study of long-term GNSS time series provides valuable insights for researchers in the field of earth sciences. Understanding the trends in these time series is particularly important for geodynamic researchers focused on earth crust movements. Functional and stochastic models play a crucial role in estimating trend values within time series data. Various methods are available to estimate variance components in GNSS time series. The least squares variance component estimation (LS-VCE) method stands out as one of the most effective approaches for this purpose. We introduce an innovative method, which streamlines calculations and simplifies equations, and therefore significantly boosting the processing speed for diagonal(ized) cofactor matrices. The method can be applied to the GNSS time series of linear stochastic models consisting of white noise, flicker noise and random walk noise. Moreover, unlike the conventional approaches, our method experiences high computational efficiency even with an increase in the number of colored noise components in time series data. For GNSS time series, this variable transformation has been applied to both univariate and multivariate modes, preserving the optimal properties of LS-VCE. We conducted simulations on daily time series spanning 5, 10, 15, and 20 years, employing two general and fast modes with one and two colored noise components plus white noise. The computation time for estimating variance components was compared between the two modes, revealing a notable decrease in processing time with the fast mode.

Satellite-derived bathymetry (SDB) provides a cost-effective solution for coastal mapping, but challenges remain in model interpretability and uncertainty quantification. This study investigates the applicability of the least-squares-based deep learning (LSBDL) framework for SDB, leveraging its hybrid structure that integrates neural networks with the available least-squares theory to enhance model transparency. ICESat-2 photon-counting LiDAR was used to train depth estimation from Sentinel-2 multispectral imagery over an approximately 30 km × 30 km region of near-coastal bathymetry at Anegada, British Virgin Islands. ICESat-2 provided high-precision depth information, of which 80% were used for training and the remainder for validation. LBSDL depth estimation achieved a root-mean-square error (RMSE) of 2.74 m, representing around 10% of the maximum observed depth, with the best performance in the 2–15 m depth range. These findings demonstrate the potential of LSBDL for interpretable and reliable bathymetric mapping, highlighting ICESat-2 as a globally accessible training and validation source and advancing SDB capabilities for data-sparse coastal regions.

Fitting a smooth curve to 2D, a surface to 3D, and a manifold to 4D irregular point cloud data is becoming a common practice in many engineering and science applications. Piecewise-polynomial spline functions provide a powerful tool applicable to interpolation and approximation problems. This study presents the least squares B-spline approximation (LSBSA) theory, which is a generalized version of the spline interpolation and can be applied to any irregularly scattered point cloud data at knots specified by the user. The formulation allows to apply the well-established body of knowledge of least squares theory to the B-spline approximation. This for example has the benefit of embedding quality control measures such as hypothesis testing and proper error propagation to assess the quality of the approximation problem. The method is applicable to many 1D curve, 2D surface and 3D manifold fitting problems of which both simulated and real data are used to illustrate the efficacy of the proposed theory. In particular, its real-world applications to multi-beam echo-sounder bathymetric data, digital terrain modeling and Greenland ice sheet deformation monitoring will be highlighted. The performance of the method for linear, quadratic, cubic and quartic spline functions will be investigated. The primary application of LSBSA lies in its ability to perform 3D manifold fitting for deformation monitoring. This capability provides the possibility of monitoring changes in continuous spatial and temporal domains. The Python and Matlab source codes of LSBSA are freely accessible at https://github.com/tudelft4d/lsbsa.

High diversity seabed habitats, such as shellfish aggregations, play a significant role in marine ecosystem sustainability but are susceptible to bottom disturbance induced by anthropogenic activities. Regular monitoring of these habitats with effective mapping methods is therefore essential. Multibeam echosounder (MBES) has been widely used in recent decades for seabed characterization due to its non-destructive manner and extensive spatial coverage compared to traditional methods like bottom sampling. Nevertheless, bottom sampling remains essential to link ground truth with acoustic seabed classification. Using seabed samples and MBES measurements, machine learning techniques are commonly employed to model their relationships and generate classification maps of an extended seabed. However, limited ground truth data, resulting from constraints in regulations, budget, or time, may impede the development of robust machine learning models. To address this challenge, we applied a semi-supervised machine learning method to classify seabed sediments of a blue mussel (Mytilus edulis) cultivation area in the Oosterschelde, the Netherlands. We utilized nine boxcore samples to generate pseudo-labels on MBES data. These pseudo-labels enlarged the training data size, facilitated the training of three comprehensive machine learning algorithms (Gradient Boosting, Random Forest, and Support Vector Machine), and helped to classify the study site into mussel and non-mussel areas. We found the geomorphological and backscatter-related features to be complementary for mussel culture detection. Our classification results were demonstrated effective through expert knowledge of this cultivation area and brought insights for future research on natural mussel habitats.

Over the last decade, the aerospace industry has experienced a remarkable growth in the use of Unmanned Aerial Vehicles (UAVs) due to their low manufacturing and operational costs, adaptability, and scalability. In the context of climate change and desired emissions targets, UAVs are realistic options to complement the current freight transportation methods, and in the future, as an viable option for human mobility. UAVs are also starting to gain a place in emergency response, both in urban and rural areas, where aid or surveillance capabilities are required in short notice and a fast response is crucial. However, an important obstacle towards their broader use is the expected noise annoyance caused by UAV operations. In this study, we combine extensive outdoor measurements of drone noise with statistical modelling to predict acoustic metrics relevant to determining acoustic annoyance. Array-based measurements combined with GPS positioning information of an eVTOL fixed-wing UAV are studied under realistic operational conditions in an open field. The linear least squares theory is used to establish an empirical model and identify a set of control parameters, including acceleration and velocity, that serve as effective predictors of noise output. The results pave the way for the establishing a model that predicts drone noise for different UAV types and for different operational conditions.