Shoreline response to a sandy nourishment in a wave-dominated coast using video monitoring

Beach nourishment is a soft engineering intervention that supplies sand to the shore, to increase the beach recreational area and to decrease coastal vulnerability to erosion. This study presents the preliminary evaluation of nourishment works performed at the high-energy wave-dominated Portuguese coast. The shoreline was adopted as a proxy to study beach evolution in response to nourishment and to wave forcing. To achieve this aim, images collected by a video monitoring system were used. A nourishment calendar was drawn up based on video screening, highlighting the different zones and phases where the works took place. Over the six-month monitoring period, a total amount of 25 video-derived shorelines were detected by both manual and automated procedures on video imagery. Nourishment works, realized in summer, enlarged the emerged beach extension by about 90 m on average. During winter, the shoreline retreated about 50 m due to wave forcing. Spatial analysis showed that the northern beach sector was more vulnerable and subject to erosion, as it is the downdrift side of the groin

Nearshore hydrodynamics and morphology derived from video imagery

Tese de doutoramento, Geologia (Geodinâmica Externa), Universidade de Lisboa, Faculdade de Ciências, 2018The coastal zone is the dynamic interface between the land and the ocean. Natural processes, including wave action, flooding and coastal erosion, often endanger human occupation and the use of the littoral. It is therefore essential to improve our understanding of the physical processes occurring at the coast, particularly those related with coastal morphodynamics. Due to the complexity of the coastal environment, littoral studies should be as comprehensive as possible, covering both hydrodynamic forcing and morphological response. However, conventional in-situ survey methods involve the use of instrumentation which, due to the logistical commitments, do not provide the required time-space scales. Remote sensing methods emerge in this context as an interesting alternative solution to yield simultaneous high temporal frequency and high spatial resolution observations of the nearshore processes. Among others, shore-based video remote sensing systems have been proved, over the last three decades, as a cost-efficient and high-quality tool to support coastal scientists and managers. Video monitoring installations offer excellent spatio-temporal resolutions, in combination with cost-efficient long-term data acquisition. This dissertation aims to present new conceptual models and video imagery tools to assess nearshore morphodynamics. This objective was accomplished through the development of a set of efficient computational tools to extract synoptic hydrodynamic and morphology information from video images. Data used in this work were acquired at five different study sites located worldwide. At three sites, video data were collected from dedicated video systems installed for scientific purpose. Two more additional video data sets were derived from the acquisition of online-streaming surfcams, which are camera infrastructures installed at the coast to provide remote visual information of sea state to surf users. A stand-alone set of algorithm was built to process and to geo-reference the acquired video sequence using already existing software. In addition, the automated processing is set to produce special images, namely Timex Variance and Timestack. A first video-based technique exploited the pixel intensity variation of Timestack images to characterize nearshore hydrodynamics. The standard deviation of pixel intensity was successfully related to the spatial distribution of wave transformation domains. Therefore, shoaling, surf and swash zones could be clearly identified in the nearshore profile covered by the image. This technique provides a new tool to study the nearshore dynamics, as the extent of wave domains can be related with distinctive morphodynamic behaviour. The method can be also directly applied to Variance images, hence it offers the possibility of extending such studies to the alongshore dimension. A second methodology developed in the scope of the present work exploited the use of pixel intensity average of Timestack images to estimate wave breaking height. Breakpoint locations and pixel intensity profiles were used to define the cross-shore breaking pattern length visible on a time-averaged image, here defined as the parameter. A first approach coupled to the available bathymetry to solve a simple conceptual model for finding breaker height. Wave breaking height estimates yield a Normalized Root Mean Square Error (NRMSE) of 14% when compared to numerical model results, for offshore wave heights ranging from 1.6 m to 3.5 m. A second approach proposed the relationship /24 to replace water depth parameter on the simplest wave height calculation formula, which multiplies water depth by the breaker index. The technique can be directly applied on Timex, therefore images from four different sites were used to test its validity, obtaining an NRMSE of about 22% for a wide range of wave heights. A third methodology aimed to investigate the possibility of combining two shorebased remote sensing techniques, 2D terrestrial LiDAR and video imagery to perform detailed beach intertidal topography. 2D LiDAR provided precise shoreline elevation along a cross-shore beach transect, while shoreline contour was detected on Timex images in the alongshore dimension. The dataset from both instruments were complemented to perform 3D beach intertidal topography mapping with a Root Mean Square Error (RMSE) of approximately 0.12 m. Finally, a method to assess nearshore bathymetry was developed. The method is based on a depth inversion technique, where wave celerity was estimated using wave trajectories visible on Timestacks. The procedure differentiates the waves in the shoaling and breaking zones and then estimates local depth from shallow or intermediate water equations. In the test case, bathymetry was mapped till a depth of 11 m with relative short time observations (5 hours), registering a RMSE of about 0.46 m when compared to ground truth data. The techniques herein developed allow to extract from video images some of the key drivers of nearshore morphodynamics, such as wave breaking height and wave period, as well as the main morphological features, namely subtidal bathymetry and intertidal beach topography. The combination of the methodologies presented in this thesis provides a comprehensive coverage of nearshore processes, enabling a synoptic representation of hydrodynamics and morphology. These methodologies may foster the implementation of new video-based operational systems and support the quasi-real time determination of coastal indicators and early warning systems for coastal hazards.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52558/201

Machine Learning Based Moored Ship Movement Prediction

[Abstract] Several port authorities are involved in the R+D+i projects for developing port management decision-making tools. We recorded the movements of 46 ships in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020. Using this data, we created neural networks and gradient boosting models that predict the six degrees of freedom of a moored vessel from ocean-meteorological data and ship characteristics. The best models achieve, for the surge, sway, heave, roll, pitch and yaw movements, a 0.99, 0.99, 0.95, 0.99, 0.98 and 0.98 R2 in training and have a 0.10 m, 0.11 m, 0.09 m, 0.9°, 0.11° and 0.15° RMSE in testing, all below 10% of the corresponding movement range. Using these models with forecast data for the weather conditions and sea state and the ship characteristics and berthing location, we can predict the ship movements several days in advance. These results are good enough to reliably compare the models’ predictions with the limiting motion criteria for safe working conditions of ship (un) loading operations, helping us decide the best location for operation and when to stop operations more precisely, thus minimizing the economic impact of cargo ships unable to operate.This research was funded by the Spanish Ministry of Economy, Industry, and Competitiveness, R&D National Plan, within the project BIA2017-86738-R, the FPI predoctoral grant from the Spanish Ministry of Science, Innovation, and Universities (PRE2018-083777) and the Spanish Ministry of Science and Innovation, Retos Call, within the project PID2020-112794RB-I00

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery

This paper investigates the potential of using a polynomial radial basis function (RBF) neural network to extract the shoreline position from coastal video images. The basic structure of the proposed network encompasses a standard RBF network module, a module of nodes that use Chebyshev polynomials as activation functions, and an inference module. The experimental setup is an operational coastal video monitoring system deployed in two sites in Southern Europe to generate variance coastal images. The histogram of each image is approximated by non-linear regression, and associated with a manually extracted intensity threshold value that quantifies the shoreline position. The key idea is to use the set of the resulting regression parameters as input data, and the intensity threshold values as output data of the network. In summary, the data set is extracted by quantifying the qualitative image information, and the proposed network takes the advantage of the powerful approximation capabilities of the Chebyshev polynomials by utilizing a small number of coefficients. For comparative reasons, we apply a polynomial RBF network trained by fuzzy clustering, and a feed-forward neural network trained by the back propagation algorithm. The comparison criteria used are the standard mean square error; the data return rates, and the root mean square error of the cross-shore shoreline position, calculated against the shorelines extracted by the aforementioned annotated threshold values. The main conclusions of the simulation study are: (a) the proposed method outperforms the other networks, especially in extracting the shoreline from images used as testing data; (b) for higher polynomial orders it obtains data return rates greater than 84%, and the root mean square error of the cross-shore shoreline position is less than 1.8 meters.JRC.H.7-Climate Risk Managemen

Cone Penetration Testing 2022

This volume contains the proceedings of the 5th International Symposium on Cone Penetration Testing (CPT’22), held in Bologna, Italy, 8-10 June 2022. More than 500 authors - academics, researchers, practitioners and manufacturers – contributed to the peer-reviewed papers included in this book, which includes three keynote lectures, four invited lectures and 169 technical papers. The contributions provide a full picture of the current knowledge and major trends in CPT research and development, with respect to innovations in instrumentation, latest advances in data interpretation, and emerging fields of CPT application. The paper topics encompass three well-established topic categories typically addressed in CPT events: - Equipment and Procedures - Data Interpretation - Applications. Emphasis is placed on the use of statistical approaches and innovative numerical strategies for CPT data interpretation, liquefaction studies, application of CPT to offshore engineering, comparative studies between CPT and other in-situ tests. Cone Penetration Testing 2022 contains a wealth of information that could be useful for researchers, practitioners and all those working in the broad and dynamic field of cone penetration testing

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion