Image-Based Classification of Double-Barred Beach States Using a Convolutional Neural Network and Transfer Learning

Nearshore sandbars characterize many sandy coasts, and unravelling their dynamics is crucial to understanding nearshore sediment pathways. Sandbar morphologies exhibit complex patterns that can be classified into distinct states. The tremendous progress in data-driven learning in image recognition has recently led to the first automated classification of single-barred beach states from Argus imagery using a Convolutional Neural Network (CNN). Herein, we extend this method for the classification of beach states in a double-barred system. We used transfer learning to fine-tune the pre-trained network of ResNet50. Our data consisted of labelled single-bar time-averaged images from the beaches of Narrabeen (Australia) and Duck (US), complemented by 9+ years of daily averaged low-tide images of the double-barred beach of the Gold Coast (Australia). We assessed seven different CNNs, of which each model was tested on the test data from the location where its training data came from, the self-tests, and on the test data of alternate, unseen locations, the transfer-tests. When the model trained on the single-barred data of both Duck and Narrabeen was tested on unseen data of the double-barred Gold Coast, we achieved relatively low performances as measured by F1 scores. In contrast, models trained with only the double-barred beach data showed comparable skill in the self-tests with that of the single-barred models. We incrementally added data with labels from the inner or outer bar of the Gold Coast to the training data from both single-barred beaches, and trained models with both single- and double-barred data. The tests with these models showed that which bar the labels used for training the model mattered. The training with the outer bar labels led to overall higher performances, except at the inner bar. Furthermore, only 10% of additional data with the outer bar labels was needed for reasonable transferability, compared to the 20% of additional data needed with the inner bar labels. Additionally, when trained with data from multiple locations, more data from a new location did not always positively affect the model’s performance on other locations. However, the larger diversity of images coming from more locations allowed the transferability of the model to the locations from where new training data were added

Metodología para el modelado y predicción del comportamiento de las barras de arena

Resumen: Las barras de arena son características naturales generadas en las zonas costeras por la interacción entre el mar y la costa. Los procesos de corto plazo que determinan el comportamiento de las barras son las olas y el transporte de sedimentos. La interacción entre el oleaje y la costa es altamente no lineal y, tradicionalmente, los modelos basados en procesos (es decir, modelos de evolución) han sido utilizados para modelar y analizar el comportamiento de las barras en el corto plazo. Sin embargo, las predicciones a mediano y largo plazo no siempre son posibles con estos modelos por las siguientes razones: i) imprecisiones en los datos usados para calibrar o validar los modelos y límites en la capacidad computacional ocasionando acumulación exponencial de errores, ii) poco entendimiento del sistema estudiado, y iii) esfuerzo computacional al simular en el mediano a largo plazo. Los modelos basados en datos surgen como una alternativa a los modelos basados en procesos, pues no se requiere el conocimiento físico para el modelo, sino que extraen el conocimiento únicamente a partir de los patrones encontrados en los datos. Las técnicas basadas en datos: descomposición empírica en modos (EMD, por su sigla en inglés) y redes neuronales autorregresivas (ARNN, por su sigla en inglés) se aplicaron a las series de tiempo de las barras y oleaje de la costa de Cartagena de Indias, Colombia para encontrar la dependencia no lineal entre ellas. El primer método se usa para analizar la relación entre las barras y las condiciones de oleaje de forma gráfica; mientras que el segundo método se usa para derivar los coeficientes de autocorrelación/correlación cruzada simple/parcial entre ellas. Se detecta evidencia de dependencias no lineales entre el estado presente de la posición de las barras y el oleaje, y el estado pasado de las barras. El principal objetivo de esta tesis es desarrollar una metodología para modelar las series de tiempo no lineales de la posición de las barras con la altura significante de ola y el período pico como variables exógenas. La metodología se basa en la clásica metodología Box - Jenkins para identificación de sistemas dinámicos lineales, pero extendiéndolo al caso no lineal. Los modelos utilizados en este enfoque están basados en redes neuronales artificiales y unos híbridos entre modelos lineales y no lineales, los cuales demuestran tener una buena capacidad de predicción.Abstract: Sandbars are natural features generated in the nearshore zones by the interaction between the sea and the coast. The short-term processes that drive sandbar behavior are waves and sediment transport. The interaction between waves and the coast is highly nonlinear and, traditionally, process-based models (e.g., evolution models) have been used for modelling and analyzing sandbar behavior in the short term. However, medium- to long-term predictions are not always possible with these models due to some reasons: i) inaccuracies in data used to calibrate or validate the model, limit on computational capacity causing exponential error accumulation, ii) little understanding of the system to be studied, and iii) computational effort when looking to mid-term and long-term simulations. Data-driven models emerge as an alternative to process-based models as they do not need insight on the physical knowledge of the model, but they extract knowledge from patterns found in the data. The data-driven techniques: EMD (empirical mode decomposition) method and ARNN (autoregressive neural networks) are applied on sandbar and wave time series from the coast of Cartagena de Indias, Colombia for finding the nonlinear dependency. The former is used for analyzing the relationship between sandbar and wave conditions in a graphical way; and the latter is used for deriving nonlinear simple/partial cross/auto-correlation coefficients. Evidence of nonlinear dependencies is detected between the present state of sandbar location and past states of wave conditions. The main goal of this thesis is to develop a methodology for modelling a nonlinear time series of sandbar position with significant wave height and peak period as exogenous variables. The methodology is based on the classical methodology by Box - Jenkins for model identification in linear dynamical systems, but extending it to the nonlinear case. The models used in this approach are based on artificial neural networks and some hybrid between linear and nonlinear show good skills for prediction.Maestrí

Optimisation of Models for the Determination of the Crest of Bars on Sandy Beaches

Sand bars are important morphological characteristics of beaches, and changes in their position and height are the main causes of profile variability. The cross-shore movement of the bars can be important for the artificial beach nourishments, because the success of the latter depends on its interaction with the bar position. Therefore, it is important to determine the location of the bars in the profile. The aim of this work is to evaluate the different existing models, and to obtain an optimized model that uses the least possible number of variables and obtains the best results. First of all, a total of 25 variables related to the characteristics of the waves, sediment and physical characteristics of the beach have been studied by means of a correlation analysis. Secondly, we have tried to generate linear models using the backward method, which generates successive models eliminating variables in each of them. These models, however, did not offer good results, with R2 values lower than 0.4. For this reason, different numerical models have been generated using among others the same variables used by different authors in their formulations or models. The numerical models of finite elements use Galerkin’s methodology and show that the most influential variables on the location of the bars crest are: wave height, period and median sediment size. These variables are very similar to those proposed by other authors; however, the formulations proposed by these authors do not offer good results in the area of study, while with the models generated, the errors committed in absolute value are less than 8%. This leads us to the final idea that the influential variables in the bars are the same in any study area, but the degree of influence or relationship with the study parameter depends on the study area.This work was partially supported by the Universidad de Alicante through the project “Estudio sobre el desgaste y composición de los sedimentos y su influencia en la erosión de las playas españolas” (GRE16-09)

Morphodynamics of single-barred embayed beaches : Shoreline and barline morphodynamics at the scale of the embayment

Seven years of shore and sandbar positions extracted from hourly time-averaged images collected at Tairua Beach (New Zealand) are used to study the morphodynamics of the shoreline and the nearshore parallel sandbar of single-barred embayed beaches. First, a semi-empirical model is proposed, that validates the concept of sandbar rotation and relates it to the wave energy gradient along the embayment. Then, a statistical study confirms the role of the alongshore wave energy flux in the rotation of embayed beach shorelines. Finally, principal component analyses and semi-empirical models are used together to characterize the dominant dynamic patterns of the shoreline and sandbar at the scale of the embayment. Dominant, simultaneous cross-shore migrations relate to beach state transitions and to variations in beach planform curvature (breathing). Rotations are largely asynchronous, confirming that different drivers are likely to be involved in shoreline and sandbar rotations

Modélisation numérique de l'évolution des profils de plages sableuses dominées par l'action de la houle

Sandbars are ubiquitous patterns along wave-dominated sandy coastlines and are key elementsin the global evolution of beaches. Cross-shore sandbar migrations are the result of the permanentimbalance between sediment flux driven by wave non-linearity and mean return current. In this thesis,we developed a new process-based beach profile model integrating the recent scientific advancesin term of hydrodynamics and sediment transport developed for beach morphodynamics. The lowcomputing time allows for long-term morphodynamic simulations (O months/years) of natural beachprofiles of diverse characteristics (beach slope, sediment grain size or type of wave breaking). Modelvalidations on several data sets, encompassing natural and experimental beach profile evolutions,highlight the respective contribution of the main hydrodynamic and sediment transport processesinvolved in specific cross-shore sandbar evolution relative to various wave conditions. Finally, all thecross-shore physical processes were integrated in a 2DH morphodynamic model, resulting for the firsttime in the simulation of a quasi-complete down state sequence showing alongshore bar generationwith subsequent spontaneous formation of transverse bar and rip morphology. These very encouragingresults pave the way for using this model to simulate 3-Dimensional evolutions of natural beachesforced by irregular wave conditionsLes barres sableuses pré-littorales ont un rôle fondamental en morphodynamique des plages soumises à l’action des vagues. Le déséquilibre permanent entre les flux sédimentaires induits vers laplage par les non linéarités des vagues et ceux induits vers le large par le courant de retour gouverne lamigration transversale des barres. Dans cette thèse, un nouveau modèle morphodynamique de profilde plage intégrant l’état de l’art des processus hydro-sédimentaires a été développé. Le faible coûten temps de calcul de ce modèle permet de réaliser des simulations à long terme, O(mois/années),de la morphologie de plages réelles ayant des caractéristiques variées (pente, type de déferlement,granularité). La simulation sur plusieurs jeux de données, de plages réelles et expérimentales, a permisd’identifier la contribution respective des principaux processus hydro-sedimentaires dans la dynamiquede la plage suivant les conditions de houle (e.g. Tempête, temps calme). Ces avancées scientifiques ontété intégrées à un modèle 2DH, ce qui a notamment permis de simuler pour la première fois sur des casacadémiques la formation d’une barre sableuse rectiligne à partir d’une plage parfaitement plane, suiviedu développement de corps sableux tridimensionnels. Ces résultats ouvrent la voie vers l’applicationde ce type de modèle aux plages naturelles soumises à une large variabilité de régimes de houle

Shoreline modelling on timescales of days to decades

Climate change is resulting in global changes to sea level and wave climates,which in many locations significantly increasethe probability of erosion, floodingand damage to coastal infrastructure and ecosystems.Therefore, there isa pressing societal need to be able to forecast themorphological evolution of our coastlines over a broad range of timescales, spanningdays-to-decades, facilitating more focussed, appropriate,and cost-effective management interventions and data informed planning to support thedevelopment of coastal environments. A wide range of modelling approaches have been used with varying degrees of success to assess both the detailed morphological evolution and/or simplified indicators of coastal erosion/accretion.This paper presents an overview of these modelling approaches, covering the full range of the complexity spectrum, summarising the advantages and disadvantages of eachmethod. A focus is given to reduced-complexity modelling approaches, including models based on equilibrium concepts, which have emerged as a particularly promising methodology for the prediction ofcoastal change over multi-decadal timescales. The advantages of stable, computationally-efficient, reduced-complexity models must be balanced against the requirement for good generality and skill in diverse and complex coastal settings. Significant obstacles are also identified, limiting the generic application of models at regional and global scales. Challenges include: the accurate long-term predictionof model forcing time-series in a changing climate, and accounting for processes that can largely be ignored in the shorter term but increase in importance in the long-term.Further complications include coastal complexities, such asthe accurate assessment of the impacts of headland bypassing. Additional complexities includecomplex structures and geology, mixed grainsize,limited sediment supply, sources and sinks. It is concluded that with present computational resources, data availability limitations and process knowledge gaps, reduced-complexity modelling approaches currently offer the most promising solution to modelling shoreline evolution on daily-to-decadal timescales

Tools for the Improvement of the Efficiency and Sustainability of Shore Nourishments – Results of the research project STENCIL

Online First, geplanter Druck 202

Storm Tide and Wave Simulations and Assessment

In this Special Issue, seven high-quality papers covering the application and development of many high-end techniques for studies on storm tides, surges, and waves have been published, for instance, the employment of an artificial neural network for predicting coastal freak waves [1]; a reproduction of super typhoon-created extreme waves [2]; a numerical analysis of nonlinear interactions for storm waves, tides, and currents [3]; wave simulation for an island using a circulation–wave coupled model [4]; an analysis of typhoon-induced waves along typhoon tracks in the western North Pacific Ocean [5]; an understanding of how a storm surge prevents or severely restricts aeolian supply [6]; and an investigation of coastal settlements and an assessment of their vulnerability [7]