Auteur moral

Auteur secondaire

Résumé

"In the application of sustainable Nature-based Solution (NbS) for coastal engineering, a significant challenge lies in determining the effectiveness of these NbS approaches in mitigating coastal erosion. The efficacy of NbS is influenced by various factors, including the specific location, layout, and the scale of implementation. This study integrates artificial intelligence (AI) with hydro-morphodynamic numerical simulations to develop an AI-based emulator focused on predicting Bed Level Changes (BLC) as indicators of erosion and deposition dynamics. Specifically, we explore the influence of seagrass meadows, varying in starting depth (hs) and depth range (hr), on coastal erosion mitigation during storm events. The framework leverages a hybrid approach combining the SCHISM-WWM hydrodynamic model with XBeach for simulating 180 depth range and starting depth combination (hr-hs) scenarios along the Norderney coast in the German Bight. A Convolutional Neural Network (CNN) architecture is employed with dual inputs?roller energy and Eulerian velocity?to predict BLC efficiently. The CNN demonstrates high accuracy in replicating spatial erosion patterns and quantifying erosion volumes, achieving an RMSE of 3.47 cm and an R² of 0.94 during validation. This innovative integration of AI and NbS not only reduces computational costs associated with traditional numerical modelling but also enhances the feasibility of What-if Scenarios applications for coastal erosion management. The findings underscore the potential of AI-driven approaches to optimize seagrass transplantation layouts and inform sustainable coastal protection strategies effectively. Future advancements aim to further streamline model integration and scalability, thereby advancing NbS applications in enhancing coastal resilience against environmental stressors."

Editeur

Elsevier

Descripteur Urbamet

Descripteur écoplanete

transport de sédiments ;érosion du littoral ;recherche scientifique ;restauration de site

Thème

Environnement - Paysage ;Maritime ;Risques

Texte intégral

Toward an AI-enhanced hydro-morphodynamic model for nature-based solutions in coastal erosion mitigation Nour Dammak , Wei Chen *, Joanna Staneva Department of Hydrodynamics and Data Assimilation, Institute of Coastal Systems-Analysis and Modelling, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany A R T I C L E I N F O Keywords: Nature-based solution Coastal erosion Ai-based emulator Seagrass meadows Convolutional neural networks hydro-morphodynamic simulations What-if Scenarios A B S T R A C T In the application of sustainable Nature-based Solution (NbS) for coastal engineering, a significant challenge lies in determining the effectiveness of these NbS approaches in mitigating coastal erosion. The efficacy of NbS is influenced by various factors, including the specific location, layout, and the scale of implementation. This study integrates artificial intelligence (AI) with hydro-morphodynamic numerical simulations to develop an AI-based emulator focused on predicting Bed Level Changes (BLC) as indicators of erosion and deposition dynamics. Specifically, we explore the influence of seagrass meadows, varying in starting depth (hs) and depth range (hr), on coastal erosion mitigation during storm events. The framework leverages a hybrid approach combining the SCHISM-WWM hydrodynamic model with XBeach for simulating 180 depth range and starting depth combination (hr-hs) scenarios along the Norderney coast in the German Bight. A Convolutional Neural Network (CNN) architecture is employed with dual inputs?roller energy and Eulerian velocity?to predict BLC efficiently. The CNN demonstrates high accuracy in replicating spatial erosion patterns and quantifying erosion volumes, achieving an RMSE of 3.47 cm and an R² of 0.94 during validation. This innovative integration of AI and NbS not only reduces computational costs associated with traditional numerical modelling but also enhances the feasibility of What-if Scenarios applications for coastal erosion management. The findings underscore the potential of AI-driven approaches to optimize seagrass transplantation layouts and inform sustainable coastal protection strategies effectively. Future advancements aim to further streamline model integration and scalability, thereby advancing NbS applications in enhancing coastal resilience against environmental stressors. 1. Introduction Coastal zones play a crucial role in the global economy, significantly contributing through ports and shipping maritime industries (Saha, 2023; Chen et al., 2023a) and maritime activities such as fisheries and tourism (Bulengela, 2024; Lamine et al., 2024; Pascoe et al., 2023). They are also vital ecological habitats that support a diverse array of life forms, contributing to the planet?s biodiversity (Saha, 2023). The coastal ecosystems such as mangrove, salt marsh and seagrass, provide ecological services such as carbon sequestration and flood protection, are crucial in addressing climate change and in protecting coastal communities (Kurniawansyah et al., 2023; Xu et al., 2022; Temmerman et al., 2023; Huang et al., 2024; Unsworth et al., 2022; Hanley et al., 2020). Coastal erosion in these areas is multifaceted, leading to loss of habitat, reduction in biodiversity, a decrease in the economic value derived from coastal resources (Gomez et al., 2020), thus affecting coastal communities (Saengsupavanich et al., 2024a; De Longueville et al., 2020). The degradation of coastal ecosystems, driven by both natural pro- cesses and human activities, underscores the urgent need for effective and sustainable management strategies to preserve these valuable areas (Saengsupavanich et al., 2023). Effective management of coastal areas requires a comprehensive understanding of both the underline hydro-morphodynamics, along with the economic benefits these regions offer and the ecological func- tions they serve (Franco-Ochoa et al., 2020; Franzen et al., 2021). Addressing the challenges posed by coastal erosion necessitates an * Corresponding author. E-mail address: wei.chen@hereon.de (W. Chen). Contents lists available at ScienceDirect Applied Ocean Research journal homepage: www.elsevier.com/locate/apor https://doi.org/10.1016/j.apor.2024.104326 Received 9 July 2024; Received in revised form 8 November 2024; Accepted 11 November 2024 Applied Ocean Research 154 (2025) 104326 Available online 17 November 2024 0141-1187/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). mailto:wei.chen@hereon.de www.sciencedirect.com/science/journal/01411187 https://www.elsevier.com/locate/apor https://doi.org/10.1016/j.apor.2024.104326 https://doi.org/10.1016/j.apor.2024.104326 http://crossmark.crossref.org/dialog/?doi=10.1016/j.apor.2024.104326&domain=pdf http://creativecommons.org/licenses/by/4.0/ integrated approach that balances economic development with the preservation of ecosystems (He and Silliman, 2019). In practice, man- aging coastal erosion involves conventional engineering solutions, such as seawalls, groins, and breakwaters (Saengsupavanich, 2022; Saeng- supavanich and Pranzini, 2023; Huang and Huang, 2024). While these applications offer immediate shoreline protection, they can disrupt natural habitats and are often costly to maintain (Temmerman et al., 2023). In recent years, ecological engineering tools, known as Nature-based Solutions (NbS) such as biogenic reefs, piles, dunes, bea- ches and vegetation, have gained popularity such as an approach to mitigate storm-induced coastal hazards (e.g., Lawson et al., 2023; Morris et al., 2019; Stark et al., 2016, Saengsupavanich et al., 2024b). NbS are characterized by their sustainability and environmentally friendly ad- vantages, as they enhance local ecosystems and promoting biodiversity, offering viable alternatives to conventional methods, combining shore- line protection with ecological restoration and long-term benefits for coastal communities (Cohn et al., 2022; Morris et al., 2019). The two main approaches for studying coastal erosion are in-situ measurements and numerical modeling, each with its own set of ad- vantages and limitations. The former employs assessing shoreline changes (Balakrishnan et al., 2023), beach profiling (Yun et al., 2023), as well as aerial or satellite imagery, combined with GIS techniques (Saengsupavanich, 2021; Noamen et al., 2024; Saïdi et al., 2024; Wang et al., 2023; Balakrishnan et al., 2023; Boukhennaf and Mezouar, 2023; Valderrama-Landeros and Flores-de-Santiago, 2019). Additionally, sediment analysis provides insights into the sources of coastal material (Bianco et al., 2020). Despite their benefits, integrating these methods presents challenges due to high costs, limitation of spatio-temporal coverage, and the necessity for consistent long-term data collection (Valderrama-Landeros and Flores-de-Santiago, 2019; Wang et al., 2023; Luppichini et al., 2022). On the other hand, numerical modeling (physics-based) provides a comprehensive analysis of coastal processes by integrating a range of physical and environmental factors (Staudt et al., 2021; Toimil et al., 2020), offering a holistic perspective on coastal dynamics (Miranda et al., 2023). It is advanced in its predictive capabilities, which are crucial for forecasting erosion and planning mitigation strategies (Chen et al., 2023b; Toimil et al., 2020). However, this approach is computa- tionally demanding, requiring significant resources, time and expertise (Shamshirband et al., 2020; Coelho et al., 2020; Seok and Suh, 2018). Its complexity limits access to specialized knowledge, introducing inac- curacies in predictions (Hunt et al., 2023). Addressing these challenges through continuously validating models against observed data is crucial for improving their reliability (Agulles and Jordà, 2024), particularly in predicting erosion-sedimentation patterns (Fan et al., 2020; Nativí-Merchán et al., 2021). In recent years, the integration of Machine Learning (ML) techniques in coastal hazard management has evolved significantly. This progres- sion is marked by a series of pivotal studies. For instance, Gharagozlou et al. (2022) developed a Gaussian process regression-based emulator to predict the morphological response of subaerial beaches to storm events. This study focused on creating a predictive model that simulates the performance of XBeach morphodynamic model, trained with data from 1250 scenarios involving sea storms and beach profiles, to forecast post-storm beach states at Nags Head, North Carolina. The emulator showed promising results, when predicting eroded beach profiles for real storms in 2019, demonstrating potential for future applications in storm-induced beach erosion prediction for hazard warnings or adap- tation studies. Another recent work concerning Deep Learning (DL)-based applications in coastal dynamics prediction. Khan et al. (2024) explored the potential of using a Convolutional Neural Network (CNN) to predict long-term coastal changes driven by sea level rise and wave conditions in the Narrabeen-Collaroy Beach in Sydney, Australia. With the focus on the morphodynamic evolution of coasts and es- tuaries, CNNs were used to emulate the Delft3D hydro-morphodynamic model to predict erosion and sedimentation changes (de Melo et al., 2022.). Such an approach achieved high accuracy with reduced computational demands, demonstrating AI??s ability to replicate com- plex environmental processes and provided a promising method for predicting morphological changes in coastal and estuarine environ- ments. Additionally, Rodriguez-Delgado et al. (2019) explores the application of an AI-based model on coastal erosion mitigation using traditional conventional engineering solutions. Their work reveals Artificial Neural Networks? capacity to assess the efficacy of wave en- ergy converter farms in coastal defense. Their NN were able to optimize the layout and position of a wave farm to protect the coastline in Playa Granada in Southern Spain. These works underscore AI?s potential to enhance conventional coastal engineering practices through predictive modelling and innovative solutions. Despite the contributions of the existing studies to AI applications in coastal management, there remains a gap in the utilization of ML ap- proaches in NbS for shoreline protection. Specifically, the capability of data-driven models to replicate hydro-morphodynamic models in man- aging coastline changes through NbS has not been fully explored. For this purpose, Chen et al. (2024) designed What-if scenarios (WiS) to investigate the effectiveness of seagrass on coastal erosion mitigation. They introduced two parameters to describe the layout and coverage of seagrass meadows on a beach, enabling their model to assess the erosion reduction and identify the most optimistic seagrass configuration within the given parameter regime. This approach, however, requires repeated numerical model simu- lations, which are time- and resource-consuming. Inspired by their work, this study is part of a broader effort toward building a hybrid chain model (Fig. 1), with the ultimate goal of developing a hydro- morphodynamic emulator for NbS aimed at mitigating coastal erosion. To be more specific, this work intends to build a CNN-based model that learns from input variables to reproduce the Bed Level Changes (BLC) utilizing WiS experiments of different seagrass layouts as training data. Details of the CNN model development is explained in section 2. This section also provides an overview of the hydro-morphodynamic model, its application in the case study of a real storm, as adopted from Chen et al. (2024). Section 3 presents the results of the experiments simulated with the emulator and are compared with the ground truth data. Section 4 discusses the sensitivity of the emulator to different data sizes and its stability over multiple simulations, as well as limitations and outlook of this work. Finally, the main findings are summarized in Section 5. 2. Methods and materials In this study, we build up a numerical and AI-based hybrid simula- tion chain that considers four main blocks (Fig. 1). The first block con- sists of numerical models implementing hydro-morphodynamic simulations. This part of work was adapted from Chen et al. (2024), that comprises two levels of simulation packages. The first level is a regional-scale circulation-wave coupled hydrodynamic model SCHISM-WWM that simulates the processes of a specific storm and provides boundary forces for the morphodynamic model XBeach to apply at the next level, which simulates nearshore morphological evo- lution. The second block contains the design of NbS. A series of exper- iments are designed that considering different layout and coverage of seagrass meadow on a study beach. The numerical models that are performed on the experiment block provide data to the AI model block for training and testing. These variables include wave indicator, roller energy; the circulation indicator, Eulerian mean velocity and the mor- phodynamic indicator, erosion/deposition. The last block corresponds to the implementation of a CNN model, trained by the outputs generated from the previous experiment blocks, allowing to reproduce the erosion/ sedimentation with ML-based approach. Details of each block of this hybrid model architecture are further elaborated in the following subsections. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 2 2.1. Numerical model framework & experiment design The model domain encloses the region in the North Sea with the area of focus being the southern German Bight between the Ems and Weser estuaries (Fig. 2i-a). The SCHISM-WWM (Roland et al., 2012; Zhang et al., 2016), with its variable spatial resolution from 1.5 km to 50 m, simulates the circulation and wave processes under storm conditions. This model is forced with sea surface elevation, 3D velocity, tempera- tures, and salinity obtained from the Geesthacht Coupled Coastal Model System 35 (GCOAST35) (Bonaduce et al., 2020; Staneva et al., 2021). The atmospheric data are provided by the German Weather Service (DWD) Consortium for Small-scale Modelling (COSMO) model, and river discharge data for the Ems, Weser, and Elbe are obtained from the German Waterways and Navigation Administration (WSV). At the regional level, the hydrodynamic-wave coupling model setup for the German Bight of the North Sea is used as a prototype, based on Stanev et al. (2019). This model has been rigorously calibrated and widely applied, as demonstrated in studies by Jacob and Stanev (2021), Jacob Fig. 1. Flow chart of the hybrid model chain design. Fig. 2. Panel (i) Domain and bathymetry of (a) the SCHISM-WWM Model and (b) a zoom in view of the study site in Norderney. Erosion/deposition area simulated by the XBeach model, with a comparison to patterns predicted by the methodology described in this paper is highlighted by white frame c. Panel (ii) Experiment design of the scenario for different combinations of initial planting depth ?hs? and planting range ?hr?. Panel (iii) An example of seagrass distribution (in white colour) for a given ?hr? and ?hs? (figures adapted from Chen et al., 2024). N. Dammak et al. Applied Ocean Research 154 (2025) 104326 3 et al. (2023), and Chen et al. (2023b). The model setup has been applied to research in assessing the effectiveness of seagrass on coastal protection in the Wadden Sea area (Jacob et al., 2023). Near the coast, the process-based model XBeach (Roelvink et al., 2009) is applied to simulate geomorphic dynamics on the Norderney coast (Fig. 2i-b). The model domain extends approxi- mately 5 km in the alongshore direction and 6 km in the cross-shore direction, with a horizontal spatial resolution of 10 × 10 m. It is forced by hourly resolved sea surface elevations and time-varying Joint North Sea Wave Project (JONSWAP) spectra from the SCHISM-WWM coupled model. The bathymetry for the XBeach model is obtained from high-resolution routine measurements made by the Lower Saxon State Department for Waterway, Coastal and Nature Conservation-Coastal Research Station at Norderney (NLWKN?CRS). This data enables not only simulation of morphodynamics, but also assessment of the effectiveness of seagrass as a NbS for coastal protec- tion. Detailed parameter settings of the numerical model block were referred to Chen et al. (2024). Initially, a reference scenario without seagrass is established to measure baseline erosion during a storm surge event from October 27 to 30, 2017. Then a series of experiments are performed, where seagrass at varying distances from the coast within previously identified erosion zones (Fig. 2i?c). The effectiveness of these measures is evaluated based on their proximity to the wave-breaking zone. By varying the depth, that starts seagrass meadow (hs) measuring from the sea surface downward and the range of the depth (hr), which specifies the coverage of the meadow (Fig. 2ii), spatial distribution of seagrass could be described by each experiment (see Fig. 2iii for example). In this work, hs varies from 1.3 to 2.4 m and hr varies from 0.1 to 1.5 m both with an interval of 0.1 m. Hence a total of 180 experiments are performed. It is important to emphasize that the focus of this study does not focus on precisely simulating bed level changes due to seagrass during storms. Instead, the primary objective is to develop an AI-based approach to enhance the efficiency of assessing seagrass impacts on coastal erosion mitigation by reducing the need for multiple hydro-morphodynamic modeling scenarios. Therefore, the validation of seagrass simulations, as conducted by Chen et al. (2024), is not detailed in this paper. 2.2. Deep learning model development 2.2.1. Data preprocessing To develop a DL model for predicting morphodynamic changes, systematic and scalable data preprocessing methodology is required. This method transforms numerical model simulation outputs into a DL- compatible format, prioritizing adaptability across diverse WiS. The following sections elaborate on the detailed process for preparing dataset, which is derived from hydrodynamic and morphodynamic simulations. 2.2.1.1. Data organization and aggregation. Data from meteorological /oceanographic models are often organized and stored in Network Common Data Form (NetCDF), encompassing multiple variables such as water levels, wave parameters, current velocities, etc. First, the temporal averages across designated time frames are calculated, reducing the time-series data to single representative values per variable. This simplification decreases data complexity, rendering the dataset more manageable for further processing while maintaining the spatial integ- rity. As our focus is on the erosion/sedimentation of the coastal zones, an important preprocessing involves calculating the BLC, i.e., the difference in bed levels at the start and end of the simulation period. 2.2.1.2. Plotting images and selecting model?s inputs. After calculating the difference of temporal average mean of the input variables that in- fluence the BLC, we identify the global minimum and maximum values for each variable across all experiments. These values are crucial as they set the standardized range for the grayscale color bar, which is used when plotting these variables in grayscale images. Plotting the data to a single grayscale channel, instead of RGB three channels, simplifies the input by reducing computational complexity while maintaining essen- tial image features. This simplification is crucial for efficient processing by the DL model, optimizing its learning capabilities and enhancing its predictive accuracy and performance. After identifying the input variables, the dataset is partitioned into distinct subsets for training and testing, with a ratio of 80:20. The training subset is used to train the CNN model, allowing it to learn the patterns and relationships within the data. Meanwhile, the testing subset remains reserved exclusively for evaluating the performance of the trained model on unseen data. 2.2.2. CNN model architecture and configuration The DL architecture designed for this study (Fig. 3) intricately pro- cesses hydro-morphodynamic data through a CNN framework, focusing on two key input features. This CNN model was built using TensorFlow (Pang., Nijkamp., and Wu 2020), with support from the Python lan- guage. The network structure is designed to capture and identify com- plex spatial patterns through images features (Deng et al., 2009), especially in coastal environments. This is achieved by employing a dual-input strategy, with each input representing different physical parameter captured in 92 × 460 pixel images. In the first stage of the network, each input undergoes a series of two- dimensional convolutional layers (CL), Conv2D. These layers utilize 32 and 64 filters of size 3 × 3, respectively. The role of these filters is to meticulously scan the input images, capturing essential spatial features that are foundational for understanding the raw data. Filters achieve this by focusing on specific patterns, such as edges or textures, which are crucial for the initial step of feature extraction (Abbass et al., 2021; Barbhuiya, Karsh and Jain, 2021). Following each convolution, the Rectified Linear Unit (ReLU) activation function introduces non-linear properties to the network. ReLU provide non-linear transformations of input neuron signals to outputs, enabling the network to learn complex patterns and perform non-linear mapping, fostering artificial intelli- gence (Wang, Li, Song, and Rong, 2020). This non-linearity is essential for distinguishing between features that cannot be separated linearly (Banerjee, Mukherjee and Pasiliao, 2020). Subsequent pooling layers (MaxPooling2D, Fig.3), employing a 2 × 2 pooling size, further process the data. These layers reduce the dimen- sionality of the data, emphasizing prominent features while suppressing less significant ones, ultimately enhancing the network?s ability to recognize patterns. This process improves computational efficiency by reducing the volume of data for subsequent layers (Gholamalinezhad and Khosravi, 2020). Overall, this ?orchestrated ?operation of convolu- tional layers, ReLU activations, and pooling layers sets a robust foun- dation for the network to efficiently learn from complex datasets. When the two data streams converge via a concatenation layer (Fig. 3), the network acquires the ability to integrate and interpret the aggregate characteristics and the combined influence of the two input variables on BLC. This fusion is essential, as it enables the combination of features and offers an integrated perspective on the dynamic re- lationships among the hydro-morphodynamical elements involved. Subsequent convolutional layers further refine the integrated features, employing both standard and transpose convolutions, equipped with 128 and 64 filters, respectively. These layers continue the feature extraction process, balancing depth and spatial resolution within the network. The transpose convolutional layers (Conv2DTranspose) in particular, play a crucial role in upscaling the feature maps, gradually reconstructing the spatial dimensions to output a final prediction image that mirrors the input resolution (Mohammed and Chen, 2022). The network?s output layer applies a linear activation function to produce a single-channel image, representing the predicted BLC. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 4 2.3. Model performance evaluation metrics The fundamental components crucial for the training process of the CNN model are explored in this work, including the loss function, optimizer, and error metrics. These elements are essential for deter- mining the model?s effectiveness from the provided data. Loss Function: The Mean Absolute Error (MAE) is selected as the loss function for its efficacy in regression scenarios (Qi et al., 2020). Defined as the average of absolute differences between predicted and actual values (Eq. (1)), MAE provides a straightforward measure of the model?s prediction accuracy across the training dataset. This process enhances the model?s ability to accurately forecast continuous variables, such as BLC. MAE = (1/n) ?n i=1 |yi ? yi| (1) where y denotes the target value of BLC, ?y is the BLC output produced by the CNN and n is the number of data points. Optimizer and learning rate: The model is compiled with the Adam optimizer (ADAptive Moment estimation), which is known for its adaptability and comprehensive features. It is distinguished by its ease of implementation, computational efficiency, and minimal memory re- quirements, rendering it widely adopted and recognized as one of the fastest optimizers for ML techniques (Hassan et al., 2023). Moreover, its notable capacity to adapt learning rates for each parameter indepen- dently ensures efficient and quick optimization (Reyad, Sarhan, and Arafa, 2023; Arora and Mishra, 2024). Following early work, the learning rate was set to 0.0001 to optimize model performance in this study. Accuracy assessment tools: The Root Mean Square Error (RMSE), widely recognized as a standard accuracy metric (Liemohn et al., 2021), is employed as an additional evaluation criterion, supplements the pri- mary loss function. It computes the average magnitude of errors, as illustrated in Eq. (2). RMSE = ?????????????????????????????????????? (1/n) ?n i=1 (yi ? yi) 2 ? (2) This parameter is calculated subsequent to the normalization of pixel values from the pixel range back to the original scale of BLC, thus has a unit corresponding to the predicted variable (meters). A lower RMSE value indicates better predictive accuracy. To further enhance the evaluation framework, we also incorporate R- squared (R²) as an accuracy metric. R-squared (Eq. (3)) measures the fitness of the prediction model and the deviation between predicted and actual values, ranging from 0 to 1 (Tukymbekov et al., 2021). A higher R² value, closer to 1, indicates higher prediction accuracy. R2 = 1 ? ?n i?=1(yi ? yi) 2 ?n i=1(yi ? y)2 (3) Finally, the model was structured to train for a maximum of 200 epochs. A truncation (Early Stopping method) was applied once there are no significant improvements in the training data loss to prevent overfitting. 3. Results 3.1. Training data outcomes Fig. 4 presents a sample of training data derived from four experi- ments, selected to span a spectrum of seagrass implementation condi- tions from deep-narrow to shallow range, each consisting of input and output sets. It shows the spatial distribution of the selected variables in alongshore (x-axis) versus across shore (y-axis). This dataset was carefully curated for the CNN model prior to the preprocessing phase described in Section 2.2.1. During preprocessing, images were converted to grayscale by reducing color channels, which ensures compatibility with the CNN architecture and enhances performance. The first column of images (Input 1) illustrates the differences in temporal mean velocity, averaged across all time steps, between Experiment i and the reference run (without seagrass). In these images, black values represent a positive difference, and white values indicate a negative difference between the temporal mean velocity of Experiment i and the reference run. The second column (Input 2) shows the differ- ences in the temporal mean of roller energy, again averaged over all time steps, between Experiment i and the reference run. Like the first column, black color denotes a positive difference, while white values signify a negative difference. The third column (Target Data) depicts BLC be- tween the first and last time steps (before and after the storm) for each experiment i. Black values indicate erosion, white values signify depo- sition, and grey tones denote areas without significant erosion or sedimentation. 3.2. Testing results The training process is conducted on a single Nvidia A100 GPU using the Levante system in German Climate Computation Center (DKRZ). The Fig. 3. CNN model architecture diagram. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 5 entire process, including the training, generation of testing data, and the calculation of accuracy metrics, typically takes <30 min. This fast pro- cessing time is a significant benefit of our AI-based approach, which aims to reduce the computational resources required compared to traditional modeling techniques. 3.2.1. AI-based model accuracy assessment Within the range of possible BLC, from ? 2 to +2 m, as shown in Fig. 5, the CNN model?s predictive capability has been evaluated with high precision. The MAE is recorded at 1.54 cm (0.01543 m), demon- strating the model?s its finely-tuned predictive accuracy. This low MAE value is particularly noteworthy as it serves as the primary loss function during the model?s training, directly influencing the optimization of its predictive capabilities. The RMSE is recorded at 3.47 cm (0.03472 m), a value that, in the context of the total 4-meter span of BLC, denotes a high level of precision in the model?s predictions. The coefficient of deter- mination, with an R² value of 0.941, asserts that the model?s predictions are closely aligned with the data, confirming that most of the variance has been effectively captured. The data points of BLC are further scattered in Fig. 5, where the x- axis represents the XBeach values and the y-axis represents the predicted ones from CNN model, both measured in meters. The dashed diagonal line represents the line of perfect prediction where the predicted values match the real values. The density of points along this line demonstrates strong prediction accuracy for this model, although some visible spread indicates variance in the prediction accuracy, especially for higher values. It is noteworthy that a substantial number of data points, exceeding 1.4 million, signifying a large dataset used for this model?s testing. 3.2.2. Outputs? visual comparison The visual representation provided in this subsection offers a comparative analysis of XBeach modeled vs. CNN predicted BLC, high- lighting the emulator?s adeptness at capturing the spatial dynamics of sedimentation and erosion within the study area. Fig. 6 compares six different hr-hs combinations spanning from shallow- large to deep-narrow range of seagrass transplantations. The left image of each pair depicts the BLC simulated by the XBeach model, which serves as the Ground Truth. On the right, the CNN-based emu- lator?s predictions are displayed for comparison. The seismic color map indicates contrast between warm and cool tones allows for a clear distinction between positive and negative BLC values, signifying erosion and deposition, respectively. The emulator accurately captures the BLC with a nuanced sensitivity to the seagrass implementation conditions in each experiment (Fig. 6). As shown in the top panel, where the seagrass has deep-narrow range, the model predicts deposition and erosion areas that closely match the XBeach model, capturing the pattern of erosion/sedimentation with good precision. As the seagrass becomes shallower and the range in- creases, the emulator adjusts its output by increasing the areas of red to reflect more erosion, indicating a keen understanding of the changing dynamics due to the reduced seagrass depth. In the shallowest seagrass implementations, seen in the bottom experiments, the CNN model still manages to closely approximate the XBeach output. Furthermore, the CNN model also demonstrates a significant/substantial capacity to detect discontinuities in the BLC due to seagrass layout by revealing clear boundaries where these discontinuities occur. These are especially noticeable in transitions between red and blue, illustrating the emula- tor?s sensitivity to changes in the configuration layout of seagrass. It is evident that the CNN model has developed during its training process, the ability to capture the finer filaments of BLC that depict the small- scale features representing uncertainty in the XBeach model. Fig. 7 presents a visual aspect of the CNN-based model?s ability to quantify BLC in the study area across three layouts scenarios, each defined by specific hr-hs combinations: 1.6?0.2, 1.4?0.2, and 1.4?0.4 m. The side-by-side comparison with the XBeach model?s outputs illustrates the emulator precision in capturing and quantifying the erosion/ Fig. 4. Example of training data. Fig. 5. Scatter plot of BLC: XBeach against Emulator. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 6 deposition patterns with fine resolution. The selection of the Jet color scale as the color map is intentionally chosen for its effective dis- tinguishing BLC intensities, from significant erosion to substantial deposition, represented by a transition from deep blues to bright reds. To enhance visual clarity and avoid potential confusion between erosion and deposition areas (where slight changes could result in similar colors), a specific BLC range from ? 0.2 to 0.2 m was masked. This allows for a focus on the most significant changes, ensuring that critical values are emphasized and clearly distinguishable. The color mapping is designed to clearly highlight the emulator?s capability; shades ranging from green to red pinpoint areas of increased erosion, with the most intense erosion approaching 2 m in red color, while the spectrum from turquoise to deep blue denotes areas of substantial deposition, with the deepest blue reflecting values close to ? 2 m. This careful coloration strategy ensures that the more pronounced BLC are readily identifiable, enhancing the comparison across the three experi- ments. The emulator?s consistent detection of hotspot values and its reliability in mirroring the spatial patterns from the XBeach model?s outputs confirm its accuracy in reflecting areas of critical BLC. 3.2.3. Reconstruction of erosion reduction regime map To investigate the effectiveness of a nature-based approach to coastal Fig. 6. Comparison between the BLC simulated by the XBeach model and the emulator going from Deep-narrow to shallow-large range of seagrass implementation. Fig. 7. Comparative visualization across 3 experiments: BLC analysis using JET color mapping with masking ? 0.2 to 0.2 m Range for Enhanced Erosion- Deposition Contrast. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 7 protection, computing the beach erosion reduction due to potential seagrass meadow layout is required. This was studied by performing XBeach modeling with all possible hs-hr combinations and comparing the erosion volume to the non-seagrass condition (Chen et al., 2024). Here we reconstruct the hs-hr regime map via our emulator and compare the results with that obtained from the XBeach model (Fig. 8). The colorbar to the right of the plots quantifies the erosion reduction per- centage E, ranging from 0 to 100 %, with 100 % indicating the greatest reduction in erosion as compared to the reference run. The TRUE plot, derived from XBeach data, shows a gradation of colors where regions of yellow correspond to values close to 100 %, suggesting areas of maximum erosion control efficacy, while areas in dark blue indicate regions where the erosion reduction is lower, with E values closer to 0. Notably, there is a concentration of yellow in the bottom-right corner band of the plot, which gradually transitions into greens and blues to- wards the edges, indicating a decrease in erosion reduction effective- ness. The predicted plot generated by the CNN model mirrors this gradation, albeit with some subtle differences in the spread and intensity of colors. The bottom-right area maintains high E values, as indicated by the persistent yellow color, demonstrating the CNN?s ability to replicate the trends observed in the XBeach data. This consistency highlights the model?s capacity to accurately forecast erosion reduction across the different seagrass layouts. The black dots on these plots represent spe- cific hr-hs combinations from the testing data experiment. Their place- ment within similarly colored regions on both plots underscores the model?s accuracy; where these dots align in color, it confirms that the emulator?s predictions are closely aligned with numerical model data. This close correspondence serves as a reliable validation of CNN?s utility in practical applications, where such predictive capability can signifi- cantly help in planning and optimizing seagrass planting strategies for effective coastal protection. 4. Discussion 4.1. Sensitivity of the AI model to training data size One critical component in developing AI-based models is the volume of training data, which ensures that the model comprehensively learns from various features and effectively predicts new data. This is partic- ularly challenging in ocean modeling, where creating a large training dataset through hydro-morphodynamic numerical models like XBeach and SCHISM is computationally demanding and time-consuming. For this study, 20,736 CPU cores and 576 h were required to generate 144 experiments for training the model. Previous studies (Ma et al., 2015; Dong et al., 2021; Syed et al., 2021) have shown that the size of the training data affects numerous factors, including the capture of spatial features in input images and their cor- relation with target data. To explore this, the impact of training data volume on the accuracy of the CNN model was analyzed by focusing on RMSE and R2. These two metrics were assessed for each simulation across various numbers of training images, as detailed in Fig. 9. The findings demonstrate that changes in training size influence model accuracy. A reduction in training data size from 140 to 70 images leads to a slight increase in RMSE (from 3.427 cm to 4.424 cm) and a small decrease in R2 (from 0.942 to 0.905). However, a reduction from 70 to 10 images results in a sharp decline in R2 (from 0.905 to 0.563) and a significant increase in RMSE (from 4.424 cm to 9.473 cm). These trends suggest that an optimal training size of 70 images per variable maintains a high predictive accuracy, with R2 above 90 % and RMSE below 4.4 cm, which is crucial for ensuring accuracy and reliability in predicting BLC. In essence, the integration of the AI block significantly enhances the efficiency of BLC prediction associated with seagrass sce- narios. With this AI block, the accuracy of BLC predictions can be maintained at up to 90 %, while reducing the number of replicate ex- periments by half. Even when the number of experiments is reduced by 75 % (to 35 experiments), the model still retains sufficient predictive accuracy at 85 %. Fig. 10 further demonstrates the model capability in accurately replicating erosion and deposition patterns with merely half the number of training samples, affirming the model?s robustness and its efficient use of data. This highlights the emulator?s potential for appli- cation in scenarios where data collection is challenging, ensuring both resource conservation and operational effectiveness. 4.2. Stability of the model During the training phase, the dataset of 180 experiments was divided into training and testing subsets based on hr-hs combination within various seagrass layouts. This partitioning was designed to expose the training set to a wide range of features, maximizing the CNN model?s learning potential. The testing subset was selected to encompass diverse scenarios, ensuring a representative evaluation of the model across different WiS. This selection approach ensures that the model is well-trained on extensive data variations and rigorously tested against diverse combinations to verify its effectiveness. To investigate the stability and robustness of the emulator, we ran- domized the splitting process by varying the random state parameter in the train_test_split function from the sklearn ML library. This function splits arrays into random train and test subsets, reflecting a more un- predictable, real-world data distribution (?Hackeling, 2017). We con- ducted 20 distinct simulations using varied random states to understand how the model performs under different data divisions. The RMSE and R2 were calculated for each scenario, and their distribution was visu- alized in Fig. 11a. The statistical analysis of these metrics shows an average RMSE of 4.03 cm, with variations between 3.51 cm and 4.77 cm. Similarly, the average R2 was 0.92, with a range from 0.89 to 0.94. Given that the BLC in the target data spans up to 4 m, these results are impressive. An average RMSE of slightly over 4 cm represents a 1 % error relative to the total BLC range, indicating a high degree of Fig. 8. Comparative Contour Analysis of the reduction of erosion with respect to the erosion from the default run (E) for experiments as a function of hr-hs. The black dots indicate experiments with different hr-hs combinations. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 8 accuracy. The RMSE and R2 are merely fluctuated across different random states, reinforcing the model?s stability and reliability across various splits. It demonstrates that the CNN model maintains high per- formance even when handling subsets of data that may not be ideally distributed. To further assess the stability of the emulator, 20 consistent runs of training and accuracy assessment were conducted using the same datasets as in Section 3.2. This approach was specifically designed to test the model?s consistency and reliability over multiple runs under identical conditions. The Analysis of RMSE and R2 presented in Fig. 11b revealed only minor fluctuations, demonstrating intrinsic stability of the model. Furthermore, the consistent performance across these evalua- tions is significant since it illustrates the model?s resilience to potential disruptions that could affect results in real-world settings (Zhang et al., 2018). This includes variations in computational power, fluctuations in hardware performance, or variation in model weights and input noise in data loading and processing (Zhang et al., 2018; Krishnan et al., 2022). Such uniformity ensures that the model can handle these variations Fig. 9. R2 and RMSE versus training size. Fig. 10. Comparison between the BLC simulated by the XBeach model and the emulator of 3 different hr-hs combinations, and with training size=75. Fig. 11. a: Model Stability Across Multiple Random State values. b: Model Stability Across multiple runs. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 9 without significant loss of accuracy, making it a reliable choice for de- ployments where consistent outcomes are essential. 4.3. Limitations and future enhancements of the emulator A primary limitation of the emulator is its reliance on a structured framework, which does not easily adapt to the unstructured data often present in diverse coastal environments. Additionally, the XBeach model is driven by the SCHISM-WWM output, i.e., water level and wave spectrum. This poses a limitation because the resolution of the spatial data used by the model may not be adequate for regions with complex geographical features (e.g., Simmons et al., 2019) or where finer scale resolutions are crucial (e.g., Miesse et al., 2023; Jacob and Stanev, 2021). These factors limit the immediate real-world applicability of this research. However, the aim of the hybrid model chain, as mentioned in Section 1 and shown in Fig. 1, is to replicate morpho-hydrodynamic simulations with nature-based solutions using AI techniques. This work focuses on identifying variables that are closely related to key sedimentation and erosion patterns. The next phase will involve downscaling spatial data to capture critical details across various envi- ronments, which presents further challenges. Recently, several AI-based techniques have been employed in the spatial downscaling of multiple oceanographic variables, such as sea surface height and ocean currents (e.g., Archambault et al., 2022, Thiria et al., 2023; Zhang et al., 2024) and wave fields (Kuehn et al., 2023; Wu et al., 2024; Zhu et al., 2023), which in turn, could provide forcing boundary for XBeach model and then acquiring the ability to mimic the numerical model block of the hybrid model chain (see Experiment block 1 in Fig. 1), thus addressing issues of data structure and spatial resolu- tion. Once this model has been implemented, another limitation arises, namely the high computational time and resources required to simulate roller energy and velocity outputs in response to different seagrass layouts, restricting the flexibility and adaptability of the model to new and varying conditions without significant reconfiguration. Hence, to overcome this challenge, a supplementary AI-based model block is needed to generate these two variables corresponding to different sea- grass layouts, based on a single reference run (without seagrass), which in turn serves as inputs to the current CNN-based model (see Experiment block 2 in Fig. 1) thereby reducing the need for multiple XBeach simulations. At present, the emulator?s practicality for coastal engineers is limited by its dependence on XBeach simulations for initial data. It does not offer immediate reductions in time and computation costs, as the initial calibration still relies on the same simulations it aims to complement. While the emulator allows for experimentation with seagrass configu- rations and better understanding of complex coastal system interactions, its current value as a practical tool is constrained. The real potential of this approach lies in future development, where improvements to the model could reduce its reliance on XBeach simulations and increase its utility as a faster and a more flexible tool for NbS optimization. Given these considerations, applying our current emulator across different coastal zones requires supplementary AI-based models and conducting rigorous testing. This would involve refining the model by adjusting its parameters to ensure optimal performance in different settings. We aim to develop an AI model that uses only a single reference simulation (without seagrass), enabling training across various seagrass layouts. This approach will assess the model?s applicability across different locations, which will eventually eliminate the need for multiple XBeach simulations. By ensuring the model can be used in various coastal areas and storm events, it opens the door to real-world appli- cations. To achieve this, the emulator must be rigorously tested and validated against real-world data from different coastal environments to ensure accuracy and reliability. However, this testing is beyond the scope of the present study. Furthermore, future work could explore integrating time de- pendency by averaging each input variable between two consecutive time steps (t, t + 1) to predict BLC within the same timeframe. This could be achieved by implementing Long Short-Term Memory layers (Hochreiter and Schmidhuber, 1997), which are effective neural net- works for time series data and widely applied in climate and ocean prediction (Li et al., 2020; Moskolaï et al., 2020; Sousa, 2022; Salman et al., 2018; Yang et al., 2017). 4.4. Seagrass planting challenges Seagrass planting in real-world coastal environments faces signifi- cant challenges due to factors such as local climate, human activities (e. g., pollution, dredging), and environmental forces like storms and cur- rents, which can negatively impact the growth and survival of seagrass meadows (van Katwijk et al., 2016; Dinu et al., 2023). These challenges, combined with the complexities of sediment transport and coastal morphodynamics, make it difficult to establish and maintain seagrass in exposed coastal areas. By simulating the effects of seagrass on coastal erosion through hydro-morphodynamic models, our study predicts optimal planting and maintenance conditions while reducing the need for many computationally expensive simulations. This addresses one of the key limitations in earlier studies, such as the high computational demands noted by Chen et al. (2024). However, this study does not consider factors like seagrass loss due to wind, wave, and current drag (Rupprecht et al., 2017), uprooting from seabed erosion (Bouma et al., 2009), and the recovery ability of seagrass after experiencing multiple storms (Olesen and Sand-Jensen, 1994; Oprandi et al., 2020), which limit the direct application of our approach in real-world scenarios. Despite these limitations, as a ?What-if? scenario-based study, this work lays the groundwork for integrating nature-based solutions into coastal management, building on research into seagrass effectiveness in miti- gating coastal erosion (Potouroglou et al., 2017; Twomey et al., 2022). 5. Summary This study presents a CNN-based model that demonstrates high predictive accuracy in replicating coastal erosion and deposition pat- terns, achieving an RMSE of 3.47 cm in the context of the total 4-meter span and R² of 0.94. The model?s performance is largely driven by the selection of key inputs, roller energy and Eulerian velocity?which are closely correlated with sedimentation and erosion processes. The iden- tification of these variables lays a strong foundation for future devel- opment, where replicating these inputs under different scenarios will enable the model to handle a wider range of coastal applications. The real strength of this approach lies in its potential for scalability and adaptability, enabling more flexible "What-if Scenario" applications that can support NbS for coastal erosion mitigation on a global scale. By reducing the reliance on traditional, computationally expensive simu- lations, this model can be applied across diverse coastal regions, helping to assess and optimize NbS under varying conditions. As the model evolves, it will allow for rapid testing of different coastal management strategies, making it a valuable tool for decision-makers and engineers who aim to implement sustainable solutions to protect coastlines from erosion. Furthermore, the integration of this AI-driven model with real- world data and more complex environmental factors, such as storm in- tensity, wave patterns, and climate change projections, can further enhance its applicability. This adaptability ensures that the model can be tailored to specific local conditions, providing insights into the best practices for coastal preservation in both developed and developing regions. In the long term, the model?s ability to reduce computational costs while improving predictive accuracy could revolutionize how NbS are assessed and deployed, making sustainable coastal management more accessible and efficient worldwide. Disclaimer Funded by the European Union. Views and opinions expressed are N. Dammak et al. Applied Ocean Research 154 (2025) 104326 10 however those of the author(s) only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union, nor the granting authority can be held responsible for them. CRediT authorship contribution statement Nour Dammak: Conceptualization, Methodology, Software, Vali- dation, Formal analysis, Investigation, Data curation, Writing ? original draft, Writing ? review & editing, Formal analysis, Visualization. Wei Chen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing ? original draft, Writing ? review & editing, Visualization, Supervision. Joanna Staneva: Conceptualization, Methodology, Formal analysis, Resources, Writing ? review & editing, Supervision, Project administration, Funding acquisition. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement We acknowledge HORIZON-MISS-2021-OCEAN-05?01, EDITO- Model Lab: Lab Underlying models for the European Digital Twin Ocean (grant agreement 101093293). Wei Chen and Joanna Staneva also acknowledge the EU Green Deal project REST-COAST: Large scale restoration of coastal ecosystems through rivers to sea connectivity (grant agreement 101037097) and the EU Project HORI- ZON?CL4?2023-SPACE-01?34, Forecasting and observing the open-to- coastal ocean for Copernicus users (FOCCUS). Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.apor.2024.104326. References Abbass, M.Y., Kwon, K.C., Alam, M.S., Piao, Y.L., Lee, K.Y., Kim, N., 2021. Image super resolution based on residual dense CNN and guided filters. Multimed. Tools. Appl. 80, 5403?5421. https://doi.org/10.1007/s11042-020-09824-3. Agulles, M., Jordà, G., 2024. Quantification of error sources in wave runup estimates on two Mediterranean sandy beaches. Coast. Eng. 187, 104402. https://doi.org/ 10.1016/j.coastaleng.2023.104402. Archambault, T., Charantonis, A., Béréziat, D., Mejia, C., Thiria, S., 2022. Sea surface height super-resolution using high-resolution sea surface temperature with a subpixel convolutional residual network. Environ. Data Sci. 1, e26. https://doi.org/ 10.1017/eds.2022.28. Arora, S., Mishra, G.S., 2024. NeuroInsight: a revolutionary self-adaptive framework for precise brain tumor classification in medical imaging using adaptive deep learning. Res. Sq. https://doi.org/10.21203/rs.3.rs-4026454/v1. Balakrishnan, P., Abulibdeh, A., Abul, K., Kabir, T., 2023. Assessment of the impact of anthropogenic evolution and natural processes on shoreline dynamics using multitemporal satellite images and statistical analysis. Water (Basel) 15 (8), 1440. https://doi.org/10.3390/W15081440. Banerjee, C., Mukherjee, T., Pasiliao, E., 2020. Feature representations using the reflected rectified linear unit (RReLU) activation. Big Data Min. Analytics 3 (2), 102?120. https://doi.org/10.26599/BDMA.2019.9020024. Barbhuiya, A.A., Karsh, R.K., Jain, R., 2021. CNN based feature extraction and classification for sign language. Multimed. Tools. Appl. 80 (2), 3051?3069. https:// doi.org/10.1007/s11042-020-09829-y. Bianco, F., Conti, P., García-Ayllon, S., Pranzini, E., 2020. An integrated approach to analyze sedimentary stock and coastal erosion in vulnerable areas: resilience assessment of San Vicenzo?s coast (Italy). Water. (Basel) 12 (3), 805. https://doi.org/ 10.3390/w12030805. Bonaduce, A., Staneva, J., Grayek, S., Bidlot, J.R., Breivik, Ø., 2020. Sea-state contributions to sea-level variability in the European Seas. Ocean. Dyn. 70, 1547?1569. https://doi.org/10.1007/s10236-020-01404-1. Boukhennaf, A., Mezouar, K., 2023. Long and short-term evolution of the Algerian coastline using remote sensing and GIS technology. Reg. Stud. Mar. Sci. 61, 102893. https://doi.org/10.1016/j.rsma.2023.102893. Bouma, T.J., Friedrichs, M., Klaassen, P., Van Wesenbeeck, B.K., Brun, F.G., Temmerman, S., Herman, P.M.J., et al., 2009. Effects of shoot stiffness, shoot size and current velocity on scouring sediment from around seedlings and propagules. Mar. Ecol. Prog. Ser. 388, 293?297. https://doi.org/10.3354/meps08130. Bulengela, G., 2024. I am a Fisher": identity and livelihood diversification in Lake Tanganyika Fisheries. Tanzania. Maritime Technol. Res. 6 (3), 268718. https://doi. org/10.33175/mtr.2024.268718. Chen, W., Staneva, J., Jacob, B., Sánchez-Artiús, X., Wurpts, A., 2024. What-if nature- based storm buffers on mitigating coastal erosion. Sci. Total Environ. 928, 172247. https://doi.org/10.1016/j.scitotenv.2024.172247. Chen, J., Chen, H., Smith, T.F., Rangel-Buitrago, N., 2023a. Analyzing the impact and evolution of ocean & coastal management: 30 years in retrospect. Ocean. Coast. Manage 242, 106697. https://doi.org/10.1016/j.ocecoaman.2023.106697. Chen, W., Jacob, B., Valle-Levinson, A., Stanev, E., Staneva, J., Badewien, T.H., 2023b. Subtidal secondary circulation induced by eddy viscosity-velocity shear covariance in a predominantly well-mixed tidal inlet. Front. Mar. Sci. 10, 1105626. https://doi. org/10.3389/fmars.2023.1105626. Coelho, C., Narra, P., Marinho, B., Lima, M., 2020. Coastal management software to support the decision-makers to mitigate coastal erosion. J. Mar. Sci. Eng. 8 (1), 37. https://doi.org/10.3390/jmse8010037. Cohn, J.L., Copp Franz, S., Mandel, R.H., Nack, C.C., Brainard, A.S., Eallonardo, A., Magar, V., 2022. Strategies to work towards long-term sustainability and resiliency of nature-based solutions in coastal environments: a review and case studies. Integr. Environ. Assess. Manage 18 (1), 123?134. https://doi.org/10.1002/ieam.4484. De Longueville, F., Hountondji, Y.C., Assogba, L., Henry, S., Ozer, P., 2020. Perceptions of and responses to coastal erosion risks: the case of Cotonou in Benin. Int. J. Disaster Risk Reduction 51, 101882. https://doi.org/10.1016/j.ijdrr.2020.101882. de Melo, W.W., Pinho, J.L.D.S., Iglesias, I., 2022. Emulating the estuarine morphology evolution using a deep convolutional neural network emulator based on hydrodynamic results of a numerical model. J. Hydroinformatics 24 (6), 1254?1268. https://doi.org/10.2166/hydro.2022.068. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: a large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 248?255. Dinu, I., Bori, A.M., Gràcia, V., García-León, M., Lin-Ye, J., Stanica, A., Sánchez- Arcilla, A., 2023. Assessing the coastal protection role of seagrass meadows along a barrier beach, southern Romanian coast. J. Sea Res. 191, 102329. https://doi.org/ 10.1016/j.seares.2022.102329. Dong, S., Quan, Y., Feng, W., Dauphin, G., Gao, L., Xing, M., 2021. A pixel cluster CNN andspectral-spatial fusion algorithm for hyperspectral image classification with small-size training samples. IEEe J. Sel. Top. Appl. Earth. Obs. Remote Sens. 14, 4101?4114. https://doi.org/10.1109/JSTARS.2021.3068864. Fan, Y., Chen, S., Pan, S., Dou, S., 2020. Storm-induced hydrodynamic changes and seabed erosion in the littoral area of Yellow River Delta: a model-guided mechanism study. Cont. Shelf. Res. 205, 104171. https://doi.org/10.1016/j.csr.2020.104171. Franco-Ochoa, C., Zambrano-Medina, Y., Plata-Rocha, W., Monjardín-Armenta, S., Rodríguez Cueto, Y., Escudero, M., Mendoza, E., 2020. Long-term analysis of wave climate and shoreline change along the Gulf of California. Appl. Sci. 10 (23), 8719. https://doi.org/10.3390/app10238719. Franzen, M.O., Fernandes, E.H., Siegle, E., 2021. Impacts of coastal structures on hydro- morphodynamic patterns and guidelines towards sustainable coastal development: a case studies review. Reg. Stud. Mar. Sci. 44, 101800. https://doi.org/10.1016/j. rsma.2021.101800. Gharagozlou, A., Anderson, D.L., Gorski, J.F., Dietrich, J.C., 2022. Emulator for eroded beach and dune profiles due to storms. J. Geophys. Res.: Earth Surface 127 (8), e2022JF006620. https://doi.org/10.1029/2022JF006620. Gholamalinezhad, H., Khosravi, H., 2020. Pooling methods in deep neural networks, a review. arXiv preprint arXiv:2009.07485. Gomez, M.L.A., Adelegan, O.J., Ntajal, J., Trawally, D., 2020. Vulnerability to coastal erosion in The Gambia: empirical experience from Gunjur. Int. J. Disaster Risk Reduction 45, 101439. https://doi.org/10.1016/j.ijdrr.2019.101439. Hackeling, G., 2017. Mastering Machine Learning With Scikit-Learn. Packt Publishing Ltd. Hanley, M.E., Bouma, T.J., Mossman, H.L., 2020. The gathering storm: optimizing management of coastal ecosystems in the face of a climate-driven threat. Ann. Bot. 125 (2), 197?212. https://doi.org/10.1093/aob/mcz204. Hassan, E., Shams, M.Y., Hikal, N.A., Elmougy, S., Hassan, T., 2023. The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed. Tools. Appl. 82, 16591?16633. https://doi.org/10.1007/s11042- 022-13820-0. He, Q., Silliman, B.R., 2019. Climate change, human impacts, and coastal ecosystems in the anthropocene. Current Biol. 29 (19), R1021?R1035. https://doi.org/10.1016/j. cub.2019.08.042. Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Comput. 9 (8), 1735?1780. https://doi.org/10.1162/neco.1997.9.8.1735. Huang, S., Huang, Z., 2024. A quasi-three-phase flow simulation of the interactions between solitary waves and a vertical seawall installed on a sandy beach. Coast. Eng. 194, 104621. https://doi.org/10.1016/j.coastaleng.2024.104621. Huang, Y., Zheng, G., Li, X., Xiao, J., Xu, Z., Tian, P., 2024. Habitat quality evaluation and pattern simulation of coastal salt marsh wetlands. Sci. Total Environ. 945, 174003. https://doi.org/10.1016/j.scitotenv.2024.174003. Hunt, E., Davidson, M., Steele, E.C., Amies, J.D., Scott, T., Russell, P., 2023. Shoreline modelling on timescales of days to decades. Cambridge Prisms: Coastal Futures 1, e16. https://doi.org/10.1017/cft.2023.5. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 11 https://doi.org/10.1016/j.apor.2024.104326 https://doi.org/10.1007/s11042-020-09824-3 https://doi.org/10.1016/j.coastaleng.2023.104402 https://doi.org/10.1016/j.coastaleng.2023.104402 https://doi.org/10.1017/eds.2022.28 https://doi.org/10.1017/eds.2022.28 https://doi.org/10.21203/rs.3.rs-4026454/v1 https://doi.org/10.3390/W15081440 https://doi.org/10.26599/BDMA.2019.9020024 https://doi.org/10.1007/s11042-020-09829-y https://doi.org/10.1007/s11042-020-09829-y https://doi.org/10.3390/w12030805 https://doi.org/10.3390/w12030805 https://doi.org/10.1007/s10236-020-01404-1 https://doi.org/10.1016/j.rsma.2023.102893 https://doi.org/10.3354/meps08130 https://doi.org/10.33175/mtr.2024.268718 https://doi.org/10.33175/mtr.2024.268718 https://doi.org/10.1016/j.scitotenv.2024.172247 https://doi.org/10.1016/j.ocecoaman.2023.106697 https://doi.org/10.3389/fmars.2023.1105626 https://doi.org/10.3389/fmars.2023.1105626 https://doi.org/10.3390/jmse8010037 https://doi.org/10.1002/ieam.4484 https://doi.org/10.1016/j.ijdrr.2020.101882 https://doi.org/10.2166/hydro.2022.068 https://doi.org/10.1016/j.seares.2022.102329 https://doi.org/10.1016/j.seares.2022.102329 https://doi.org/10.1109/JSTARS.2021.3068864 https://doi.org/10.1016/j.csr.2020.104171 https://doi.org/10.3390/app10238719 https://doi.org/10.1016/j.rsma.2021.101800 https://doi.org/10.1016/j.rsma.2021.101800 https://doi.org/10.1029/2022JF006620 https://doi.org/10.1016/j.ijdrr.2019.101439 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0030 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0030 https://doi.org/10.1093/aob/mcz204 https://doi.org/10.1007/s11042-022-13820-0 https://doi.org/10.1007/s11042-022-13820-0 https://doi.org/10.1016/j.cub.2019.08.042 https://doi.org/10.1016/j.cub.2019.08.042 https://doi.org/10.1162/neco.1997.9.8.1735 https://doi.org/10.1016/j.coastaleng.2024.104621 https://doi.org/10.1016/j.scitotenv.2024.174003 https://doi.org/10.1017/cft.2023.5 Jacob, B., Dolch, T., Wurpts, A., Staneva, J., 2023. Evaluation of seagrass as a nature- based solution for coastal protection in the German Wadden Sea. Ocean. Dyn. 73 (11), 699?727. https://doi.org/10.1007/s10236-023-01577-5. Jacob, B., Stanev, E.V., 2021. Understanding the impact of bathymetric changes in the German bight on coastal hydrodynamics: one step toward realistic morphodynamic modeling. Front. Mar. Sci. 8, 640214. https://doi.org/10.3389/fmars.2021.640214. Khan, A.R., Ab Razak, M.S.B., Yusuf, B.B., Shafri, H.Z.B.M., Mohamad, N.B., 2024. Future prediction of coastal recession using convolutional neural network. Estuar. Coast. Shelf. Sci. 299, 108667. https://doi.org/10.1016/j.ecss.2024.108667. Krishnan, G., Yang, L., Sun, J., Hazra, J., Du, X., Liehr, M., Cao, Y., et al., 2022. Exploring model stability of deep neural networks for reliable RRAM-based in-memory acceleration. IEEE Trans. Comput. 71 (11), 2740?2752. Kuehn, J., Abadie, S., Liquet, B., Roeber, V., 2023. A deep learning super-resolution model to speed up computations of coastal sea states. Appl. Ocean Res. 141, 103776. https://doi.org/10.1016/j.apor.2023.103776. Kurniawansyah, A., Susiloningtyas, D., Manessa, M.D.M., 2023. Mangrove ecosystem management in Indonesia: review, limitation, gap, and knowledge. Maritime Technol. Res. 5 (3), 262310. https://doi.org/10.33175/mtr.2023.262310. Lamine, I., Mghili, B., Chahouri, A., Aqnouy, M., Moukrim, A., Alla, A.A., 2024. Growing coastal tourism: can biomonitoring provide insights into the health of coastal ecosystems? Mar. Pollut. Bull. 201, 116253. https://doi.org/10.1016/j. marpolbul.2024.116253. Lawson, S.K., Udo, K., Tanaka, H., Bamunawala, J., 2023. Littoral Drift Impoundment at a Sandbar Breakwater: two Case Studies along the Bight of Benin Coast (Gulf of Guinea, West Africa). J. Mar. Sci. Eng. 11 (9), 1651. https://doi.org/10.3390/ jmse11091651. Li, Y., Van Oosterom, P., Ge, Y., Zhang, X., Baart, F., 2020. A CNN?LSTM method for the morphology evolution prediction of beach mega-nourishment. IEEe Access. 8, 184512?184523. https://doi.org/10.1109/ACCESS.2020.3030119. Liemohn, M.W., Shane, A.D., Azari, A.R., Petersen, A.K., Swiger, B.M., Mukhopadhyay, 2021. RMSE is not enough: guidelines to robust data-model comparisons for magnetospheric physics. J. Atmos. Sol. Terr. Phys. 218, 105624. https://doi.org/ 10.1016/j.jastp.2021.105624. Luppichini, M., Bini, M., Berton, A., Casarosa, N., Merlino, S., Paterni, M., 2022. A method based on beach profile analysis for shoreline identification. In: Ninth International Symposium ?Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques. University Press, Firenze, pp. 47?60. https://doi.org/ 10.36253/979-12-215-0030-1.05. Ma, L., Cheng, L., Li, M., Liu, Y., Ma, X., 2015. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS J. Photogramm. Remote Sens. 102, 14?27. https://doi.org/ 10.1016/j.isprsjprs.2014.12.026. Miesse, T., de Souza de Lima, A., Khalid, A., Cassalho, F., Coleman, D.J., Ferreira, C.M., Sutton-Grier, A.E., 2023. Numerical modeling of wave attenuation: implications of representing vegetation found in coastal saltmarshes in the Chesapeake Bay. Environ. Monit. Assess. 195 (8), 982. https://doi.org/10.1007/s10661-023-11533-x. Miranda, N.A., Bintoro, R.S., Prasita, V.D., 2023. The pattern of coastline changes and wave modelling around the expansion of PPI Popoh Tulung Agung. East Java. Maritime Technol. Res. 5 (4). https://doi.org/10.33175/mtr.2023.262926, 262926- 262926. Mohammed, A.A.H., Chen, J., 2022. [Retracted] Cleanup sketched drawings: deep learning-based model. Appl. Bionics Biomech. 2022 (1), 2238077. https://doi.org/ 10.1155/2023/9891841. Morris, R., Strain, E.M., Konlechner, T.M., Fest, B.J., Kennedy, D.M., Arndt, S.K., Swearer, S.E., 2019. Developing a nature-based coastal defence strategy for Australia. Australian J. Civil Eng. 17 (2), 167?176. https://doi.org/10.1080/ 14488353.2019.1661062. Moskolaï, W., Abdou, W., Dipanda, A., Kolyang, D.T., 2020. Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series. arXiv preprint arXiv:2009.00841. https://doi.org/10.48550/arXiv.2009.00841. Nativí-Merchán, S., Caiza-Quinga, R., Saltos-Andrade, I., Martillo-Bustamante, C., Andrade-García, G., Quiñonez, M., Cedeño, J., et al., 2021. Coastal erosion assessment using remote sensing and computational numerical model. Case of study: libertador Bolivar, Ecuador. Ocean. Coast. Manage 214, 105894. https://doi.org/ 10.1016/j.ocecoaman.2021.105894. Noamen, R., Rihem, M.E.F., Zouhour, Y., Riadh, E.M., Sonia, G., 2024. Diachronic study of coastline behavior using remote sensing: a case study of Korba beach, Tunis. Euro- Mediterr. J. Environ. Integr. 1-17. https://doi.org/10.1007/s41207-024-00478-3. Olesen, B., Sand-Jensen, K., 1994. Patch dynamics of eelgrass Zostera marina. Marine Ecol.-Progress Series 106, 147?156. Oprandi, A., Mucerino, L., De Leo, F., Bianchi, C.N., Morri, C., Azzola, A., Montefalcone, M., et al., 2020. Effects of a severe storm on seagrass meadows. Sci. Total Environ. 748, 141373. https://doi.org/10.1016/j.scitotenv.2020.141373. Pang, B., Nijkamp, E., Wu, Y.N., 2020. Deep learning with tensorflow: a review. J. Educ. Behav. Stat. 45 (2), 227?248. https://doi.org/10.3102/1076998619872761. Pascoe, S., Paredes, S., Coglan, L., 2023. The indirect economic contribution of fisheries to coastal communities through tourism. Fishes. 8 (3), 138. https://doi.org/ 10.3390/fishes8030138. Potouroglou, M., Bull, J.C., Krauss, K.W., Kennedy, H.A., Fusi, M., Daffonchio, D., Huxham, M., et al., 2017. Measuring the role of seagrasses in regulating sediment surface elevation. Sci. Rep. 7 (1), 11917. https://doi.org/10.1038/s41598-017- 12354-y. Qi, J., Du, Q., Siniscalchi, S.M., Ma, X., Lee, C.-H., 2020. On mean absolute error for deep neural network based vector-to-vector regression. IEEe Signal. Process. Lett. 27, 1485?1489. https://doi.org/10.1109/LSP.2020.3016837. Reyad, M., Sarhan, A.M., Arafa, M., 2023. A modified Adam algorithm for deep neural network optimization. Neural Comput. Appl. 35 (23), 17095?17112. https://doi. org/10.1007/s00521-023-08568-z. Rodriguez-Delgado, C., Bergillos, R.J., Iglesias, G., 2019. An artificial neural network model of coastal erosion mitigation through wave farms. Environ. Model. Softw. 119, 390?399. https://doi.org/10.1016/j.envsoft.2019.07.010. Roelvink, D., Reniers, A., Van Dongeren, A.P., De Vries, J.V.T., McCall, R., Lescinski, J., 2009. Modelling storm impacts on beaches, dunes and barrier islands. Coast. Eng. 56 (11?12), 1133?1152. https://doi.org/10.1016/j.coastaleng.2009.08.006. Roland, A., Zhang, Y.J., Wang, H.V., Meng, Y., Teng, Y.C., Maderich, V., Brovchenko, I., Dutour-Sikiric, M., Zanke, U., 2012. A fully coupled 3d wave-current interaction model on unstructured grids. J. Geophys. Res.: Oceans 117. https://doi.org/ 10.1029/2012JC007952. Rupprecht, F., Möller, I., Paul, M., Kudella, M., Spencer, T., Van Wesenbeeck, B.K., Schimmels, S., et al., 2017. Vegetation-wave interactions in salt marshes under storm surge conditions. Ecol. Eng. 100, 301?315. https://doi.org/10.1016/j. ecoleng.2016.12.030. Saha, R.C., 2023. Chattogram Port: a dedicated service institution to evolve the country boldly. Maritime Technol. Res. 5 (1), 258294. https://doi.org/10.33175/ mtr.2023.258294. Saengsupavanich, C., Rif?atin, H.Q., Magdalena, I., Ariffin, E.H., 2024a. A systematic review of jetty induced downdrift coastal erosion management. Reg. Stud. Mar. Sci. 103523. https://doi.org/10.1016/j.rsma.2024.103523. Saengsupavanich, C., Ratnayake, A.S., Yun, L.S., Ariffin, E.H., 2023. Current challenges in coastal erosion management for southern Asian regions: examples from Thailand, Malaysia, and Sri Lanka. Anthropocene Coasts 6 (1), 15. https://doi.org/10.1007/ s44218-023-00030-w. Saengsupavanich, C., 2022. Successful coastal protection by step concrete revetments in Thailand. IOP Conf. Ser. Earth Environ. Sci. 1072, 012002. https://doi.org/10.1088/ 1755-1315/1072/1/012002. Saengsupavanich, C., Pranzini, E., 2023. The 2021-procedure for coastal protection by revetments in Thailand. J. Appl. Water Eng. Res. 11 (2), 303?316. https://doi.org/ 10.1080/23249676.2022.2110529. Saengsupavanich, C., Ferren, V., Magdalena, I., Ariffin, E.H., Sanitwong-Na- Ayutthaya, S., 2024b. Using piles for wave reduction and coastal protection: a review. Reg. Stud. Mar. Sci. 77, 103638. https://doi.org/10.1016/j. rsma.2024.103638. Saengsupavanich, C., 2021. Morphological evolution of sand spits in Thailand. Marine Geodesy 44 (5), 432?453. https://doi.org/10.1080/01490419.2021.1893873. Saïdi, H., Guebsi, R., Chaabani, C., Ben Haj, M., Khelifi, N., 2024. Assessment of coastal changes following the construction of a groyne using satellite and drone imagery along the Mediterranean coast of northwest Tunisia (Rafraf, Bizerte). EuroMediterr. J. Environ. Integr. 1?12. https://doi.org/10.1007/s41207-023-00456-1. Salman, A.G., Heryadi, Y., Abdurahman, E., Suparta, W., 2018. Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Comput. Sci. 135, 89?98. https://doi.org/10.1016/j. procs.2018.08.153. Seok, J.S., Suh, S.W., 2018. Efficient real-time erosion early warning system and artificial sand dune breaching on Haeundae Beach, Korea. J. Coast. Res. (85), 186?190. https://doi.org/10.2112/SI85-038.1. Shamshirband, S., Mosavi, A., Rabczuk, T., Nabipour, N., Chau, K.W., 2020. Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines. Eng. Appl. Comput. Fluid Mech. 14 (1), 805?817. https://doi.org/ 10.1080/19942060.2020.1773932. Simmons, J.A., Splinter, K.D., Harley, M.D., Turner, I.L., 2019. Calibration data requirements for modelling subaerial beach storm erosion. Coast. Eng. 152, 103507. https://doi.org/10.1016/j.coastaleng.2019.103507. Sousa, W.R.N.D., 2022. Criação e Avaliação De Modelos De Prognóstico Futuro De Linhas De costa, Utilizando Regressão Estatística e Redes Neurais artificiais, a Partir Das Séries Temporais De Imagens De Satélite (With Abstract in English). http://www. repositorio.ufc.br/handle/riufc/69728. Staneva, J., Ricker, M., Carrasco Alvarez, R., Breivik, Ø., Schrum, C., 2021. Effects of wave-induced processes in a coupled wave?ocean model on particle transport simulations. Water. (Basel) 13 (4), 415. https://doi.org/10.3390/w13040415. Stanev, E.V., Jacob, B., Pein, J., 2019. German Bight estuaries: an inter-comparison on the basis of numerical modeling. Cont. Shelf. Res. 174, 48?65. https://doi.org/ 10.1016/j.csr.2019.01.001. Stark, J., Plancke, Y., Ides, S., Meire, P., Temmerman, S., 2016. Coastal flood protection by a combined nature-based and engineering approach: modeling the effects of marsh geometry and surrounding dikes. Estuarine Coastal Shelf Sci. 175, 34?45. https://doi.org/10.1016/j.ecss.2016.03.027. Staudt, F., Gijsman, R., Ganal, C., Mielck, F., Wolbring, J., Hass, H.C., Schimmels, S., 2021. The sustainability of beach nourishments: a review of nourishment and environmental monitoring practice. J. Coast. Conserv. 25, 1?24. https://doi.org/ 10.1007/s11852-021-00801-y. Syed, F.H., Tahir, M.A., Rafi, M., Shahab, M.D., 2021. Feature selection for semi- supervised multi-target regression using genetic algorithm. Appl. Intell. 51, 8961?8984. https://doi.org/10.1007/s10489-021-02291-9. Temmerman, S., Horstman, E.M., Krauss, K.W., Mullarney, J.C., Pelckmans, I., Schoutens, K., 2023. Marshes and mangroves as nature-based coastal storm buffers. Ann. Rev. Mar. Sci. 15 (1), 95?118. https://doi.org/10.1146/annurev-marine- 040422-092951. Thiria, S., Sorror, C., Archambault, T., Charantonis, A., Bereziat, D., Mejia, C., Crépon, M., et al., 2023. Downscaling of ocean fields by fusion of heterogeneous N. Dammak et al. Applied Ocean Research 154 (2025) 104326 12 https://doi.org/10.1007/s10236-023-01577-5 https://doi.org/10.3389/fmars.2021.640214 https://doi.org/10.1016/j.ecss.2024.108667 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0041 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0041 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0041 https://doi.org/10.1016/j.apor.2023.103776 https://doi.org/10.33175/mtr.2023.262310 https://doi.org/10.1016/j.marpolbul.2024.116253 https://doi.org/10.1016/j.marpolbul.2024.116253 https://doi.org/10.3390/jmse11091651 https://doi.org/10.3390/jmse11091651 https://doi.org/10.1109/ACCESS.2020.3030119 https://doi.org/10.1016/j.jastp.2021.105624 https://doi.org/10.1016/j.jastp.2021.105624 https://doi.org/10.36253/979-12-215-0030-1.05 https://doi.org/10.36253/979-12-215-0030-1.05 https://doi.org/10.1016/j.isprsjprs.2014.12.026 https://doi.org/10.1016/j.isprsjprs.2014.12.026 https://doi.org/10.1007/s10661-023-11533-x https://doi.org/10.33175/mtr.2023.262926 https://doi.org/10.1155/2023/9891841 https://doi.org/10.1155/2023/9891841 https://doi.org/10.1080/14488353.2019.1661062 https://doi.org/10.1080/14488353.2019.1661062 http://doi.org/10.48550/arXiv.2009.00841 https://doi.org/10.1016/j.ocecoaman.2021.105894 https://doi.org/10.1016/j.ocecoaman.2021.105894 https://doi.org/10.1007/s41207-024-00478-3 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0056 http://refhub.elsevier.com/S0141-1187(24)00447-4/sbref0056 https://doi.org/10.1016/j.scitotenv.2020.141373 https://doi.org/10.3102/1076998619872761 https://doi.org/10.3390/fishes8030138 https://doi.org/10.3390/fishes8030138 https://doi.org/10.1038/s41598-017-12354-y https://doi.org/10.1038/s41598-017-12354-y https://doi.org/10.1109/LSP.2020.3016837 https://doi.org/10.1007/s00521-023-08568-z https://doi.org/10.1007/s00521-023-08568-z https://doi.org/10.1016/j.envsoft.2019.07.010 https://doi.org/10.1016/j.coastaleng.2009.08.006 https://doi.org/10.1029/2012JC007952 https://doi.org/10.1029/2012JC007952 https://doi.org/10.1016/j.ecoleng.2016.12.030 https://doi.org/10.1016/j.ecoleng.2016.12.030 https://doi.org/10.33175/mtr.2023.258294 https://doi.org/10.33175/mtr.2023.258294 https://doi.org/10.1016/j.rsma.2024.103523 https://doi.org/10.1007/s44218-023-00030-w https://doi.org/10.1007/s44218-023-00030-w https://doi.org/10.1088/1755-1315/1072/1/012002 https://doi.org/10.1088/1755-1315/1072/1/012002 https://doi.org/10.1080/23249676.2022.2110529 https://doi.org/10.1080/23249676.2022.2110529 https://doi.org/10.1016/j.rsma.2024.103638 https://doi.org/10.1016/j.rsma.2024.103638 https://doi.org/10.1080/01490419.2021.1893873 https://doi.org/10.1007/s41207-023-00456-1 https://doi.org/10.1016/j.procs.2018.08.153 https://doi.org/10.1016/j.procs.2018.08.153 https://doi.org/10.2112/SI85-038.1 https://doi.org/10.1080/19942060.2020.1773932 https://doi.org/10.1080/19942060.2020.1773932 https://doi.org/10.1016/j.coastaleng.2019.103507 http://www.repositorio.ufc.br/handle/riufc/69728 http://www.repositorio.ufc.br/handle/riufc/69728 https://doi.org/10.3390/w13040415 https://doi.org/10.1016/j.csr.2019.01.001 https://doi.org/10.1016/j.csr.2019.01.001 https://doi.org/10.1016/j.ecss.2016.03.027 https://doi.org/10.1007/s11852-021-00801-y https://doi.org/10.1007/s11852-021-00801-y https://doi.org/10.1007/s10489-021-02291-9 https://doi.org/10.1146/annurev-marine-040422-092951 https://doi.org/10.1146/annurev-marine-040422-092951 observations using deep learning algorithms. Ocean. Model. (Oxf) 182, 102174. https://doi.org/10.1016/j.ocemod.2023.102174. Toimil, A., Camus, P., Losada, I.J., Le Cozannet, G., Nicholls, R.J., Idier, D., Maspataud, A., 2020. Climate change-driven coastal erosion modelling in temperate sandy beaches: methods and uncertainty treatment. Earth. Sci. Rev. 202, 103110. https://doi.org/10.1016/j.earscirev.2020.103110. Tukymbekov, D., Saymbetov, A., Nurqaliev, M., Kuttybay, N., Dosymbetova, G., Svanbayev, Y., 2021. Intelligent autonomous street lighting system based on weather forecast using LSTM. Energy 231, 120902. https://doi.org/10.1016/j. energy.2021.120902. Twomey, A.J., Callaghan, D.P., O?Brien, K.R., Saunders, M.I., 2022. Contextualising shoreline protection by seagrass using lessons from submerged breakwaters. Estuar. Coast. Shelf. Sci. 276, 108011. https://doi.org/10.1016/j.ecss.2022.108011. Unsworth, R.K., Cullen-Unsworth, L.C., Jones, B.L., Lilley, R.J., 2022. The planetary role of seagrass conservation. Science (1979) 377 (6606), 609?613. https://doi.org/ 10.1126/science.abq6923. Valderrama-Landeros, L., Flores-de-Santiago, F., 2019. Assessing coastal erosion and accretion trends along two contrasting subtropical rivers based on remote sensing data. Ocean Coastal Manag. 169, 58?67. https://doi.org/10.1016/j. ocecoaman.2018.12.006. van Katwijk, M.M., Thorhaug, A., Marbà, N., Orth, R.J., Duarte, C.M., Kendrick, G.A., Verduin, J.J., et al., 2016. Global analysis of seagrass restoration: the importance of large-scale planting. J. Appl. Ecol. 53 (2), 567?578. https://doi.org/10.1111/1365- 2664.12562. Wang, J., Wang, L., Feng, S., Peng, B., Huang, L., Fatholahi, S.N., Tang, L., Li, J., 2023. An overview of shoreline mapping by using airborne LiDAR. Remote Sens. (Basel) 15 (1), 253. https://doi.org/10.3390/rs15010253. Wang, Y., Li, Y., Song, Y., Rong, X., 2020. The influence of the activation function in a convolution neural network model of facial expression recognition. Appl. Sci. 10 (5), 1897. https://doi.org/10.3390/app10051897. Wu, X., Zhao, R., Chen, H., Wang, Z., Yu, C., Jiang, X., Song, Z., et al., 2024. GSDNet: a deep learning model for downscaling the significant wave height based on NAFNet. J. Sea Res. 198, 102482. https://doi.org/10.1016/j.seares.2024.102482. Xu, Y., Kalra, T.S., Ganju, N.K., Fagherazzi, S., 2022. Modeling the dynamics of salt marsh development in coastal land reclamation. Geophys. Res. Lett. 49 (6), e2021GL095559. https://doi.org/10.1029/2021GL095559. Yang, Y., Dong, J., Sun, X., Lima, E., Mu, Q., Wang, X., 2017. A CFCC-LSTM model for sea surface temperature prediction. IEEE Geosci. Remote Sens. Lett. 15 (2), 207?211. https://doi.org/10.1109/LGRS.2017.2780843. Yun, L.S., Saengsupavanich, C., Ariffin, E.H., Rashidi, A.H.M., 2023. The morphodynamics of wave on a monsoon-dominated coasts: west coast of GoT. Reg. Stud. Mar. Sci. 57, 102729. https://doi.org/10.1016/j.rsma.2022.102729. Zhang, W., Li, C., Peng, G., Chen, Y., Zhang, Z., 2018. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439?453. https://doi.org/10.1016/j.ymssp.2017.06.022. Zhang, Y.J., Stanev, E.V., Grashorn, S., 2016. Unstructured-grid model for the North Sea and Baltic Sea: validation against observations. Ocean Model. 97, 91?108. https:// doi.org/10.1016/j.ocemod.2015.11.009. Zhang, Q., Sun, W., Guo, H., Dong, C., Zheng, H., 2024. A transfer learning-enhanced generative adversarial network for downscaling sea surface height through heterogeneous data fusion. Remote Sens. (Basel) 16, 763. https://doi.org/10.3390/ rs16050763. Zhu, X., Wu, K., Huang, W., 2023. Deep learning approach for downscaling of significant wave height data from wave models. Ocean. Model. (Oxf) 185, 102257. https://doi. org/10.1016/j.ocemod.2023.102257. N. Dammak et al. Applied Ocean Research 154 (2025) 104326 13 https://doi.org/10.1016/j.ocemod.2023.102174 https://doi.org/10.1016/j.earscirev.2020.103110 https://doi.org/10.1016/j.energy.2021.120902 https://doi.org/10.1016/j.energy.2021.120902 https://doi.org/10.1016/j.ecss.2022.108011 https://doi.org/10.1126/science.abq6923 https://doi.org/10.1126/science.abq6923 https://doi.org/10.1016/j.ocecoaman.2018.12.006 https://doi.org/10.1016/j.ocecoaman.2018.12.006 https://doi.org/10.1111/1365-2664.12562 https://doi.org/10.1111/1365-2664.12562 https://doi.org/10.3390/rs15010253 https://doi.org/10.3390/app10051897 https://doi.org/10.1016/j.seares.2024.102482 https://doi.org/10.1029/2021GL095559 https://doi.org/10.1109/LGRS.2017.2780843 https://doi.org/10.1016/j.rsma.2022.102729 https://doi.org/10.1016/j.ymssp.2017.06.022 https://doi.org/10.1016/j.ocemod.2015.11.009 https://doi.org/10.1016/j.ocemod.2015.11.009 https://doi.org/10.3390/rs16050763 https://doi.org/10.3390/rs16050763 https://doi.org/10.1016/j.ocemod.2023.102257 https://doi.org/10.1016/j.ocemod.2023.102257  Toward an AI-enhanced hydro-morphodynamic model for nature-based solutions in coastal erosion mitigation  1 Introduction  2 Methods and materials  2.1 Numerical model framework & experiment design  2.2 Deep learning model development  2.2.1 Data preprocessing  2.2.1.1 Data organization and aggregation  2.2.1.2 Plotting images and selecting model?s inputs  2.2.2 CNN model architecture and configuration  2.3 Model performance evaluation metrics  3 Results  3.1 Training data outcomes  3.2 Testing results  3.2.1 AI-based model accuracy assessment  3.2.2 Outputs? visual comparison  3.2.3 Reconstruction of erosion reduction regime map  4 Discussion  4.1 Sensitivity of the AI model to training data size  4.2 Stability of the model  4.3 Limitations and future enhancements of the emulator  4.4 Seagrass planting challenges  5 Summary  Disclaimer  CRediT authorship contribution statement  Declaration of competing interest  Acknowledgement  Supplementary materials  References