Modelling cross-shore shoreline change on multiple timescales and their interactions

Schepper, Rob; Almar, Rafaël; Bergsma, Erwin W. J.; Vries, Sierd de; Reniers, Ad; Davidson, Mark; Splinter, Kristen D.

doi:10.31223/x54c73

Cited by 3 publications

(3 citation statements)

References 48 publications

(94 reference statements)

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“…ShoreFor has been used in multiple shoreline prediction studies and a number of extensions have been proposed to improve its performance by accounting for shoreline change over different time-scales (Schepper et al, 2021) as well as alongshore sediment transport processes (Tran and Barthélemy, 2020;Tran et al, 2021). We make use of the ShoreFor model as a base for our experiments on the use of CGP for shoreline forecasting in a GI setting, and we highlight the possibility of extending the base CGP-ShoreFor implementation to account for these additional processes.…”

Section: Shoreformentioning

confidence: 99%

Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

Najar¹,

Almar²,

Bergsma³

et al. 2023

Preprint

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Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain the relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a shoreline prediction model. Coupled with NSGA-II, the CGP individuals are evaluated and selected during evolution according to their predictive skills at five different coastal sites. This work presents a comparison between the CGP-evolved models and the base ShoreFor model. In addition to its ability to produce well-performing models, the work demonstrates the usefulness of CGP as a research tool to gain insight into the behaviors of shorelines at different points around the globe.

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Section: Shoreformentioning

confidence: 99%

Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

Najar¹,

Almar²,

Bergsma³

et al. 2023

Preprint

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“…(5) in which 𝑛 is the number of timesteps in the considered timeseries prior to the considered timestep, Ω 𝑛 is the dimensionless fall velocity at timestep 𝑛, and ϕ is the memory decay factor. This memory decay factor determines the number of antecedent timesteps that are considered (Davidson et al, 2013;Schepper et al, 2021). A large memory decay factor generates a slowly varying timeseries, considering and averaging many prior timesteps, while a small decay factor produces a timeseries with faster oscillations.…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…Neural networks have been applied to coastal engineering problems in a few cases, e.g. wave overtopping has been predicted using neural networks (van Gent et al, 2007;Verhaeghe et al, 2008), and using GPEs (Pullen et al, 2018). Morphological changes, specifically beach volume changes, have however to our knowledge not previously been predicted using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Machine Learning-Probabilistic Approach to Predict Beach Volume Change

Pieterse,

Lara,

Habtemariam

et al. 2023

Int. Conf. Coastal. Eng.

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In the DataBeach study a machine learning model was developed with the aim to improve the efficiency and sustainability of design of soft coastal defense projects. A morphological model based on machine learning was trained and tested to predict beach volume changes with significantly reduced computational time compared to traditional process-based models. The machine learning model was then applied, combined with a ‘penalty function’ for inclusion of morphological feedback, to predict beach volume changes for the study area of approximately 2 km alongshore and on a 10-year project timescale. In order to run many different scenarios for the 10-year prediction, a probabilistic methodology was developed to take into account the uncertainties in this time period. The machine learning-based model provides great benefits for probabilistic simulations, due to the lower computational time, compared to process-based numerical models such as XBeach, and a flexible way in which it can incorporate measurement data. The performance of the tested machine learning models was comparable to that of the short term volume predictions of XBeach. Comparison of 10-year predicted volume changes using the machine learning model and measured beach topography (LiDAR) showed good agreement between measured and predicted volume changes for the dry beach area (when accounting for nourishments), but overestimation in the beach volume change predictions for the intertidal beach. These differences are partially attributed to the poor performance on XBeach for long term, normal wave conditions, which are an important factor in the intertidal area.

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Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

Tran

Marchesiello

Almar

et al. 2021

JMSE

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The present study focuses on the long-term multi-year evolution of the shoreline position of the Nha Trang sandy beach. To this end an empirical model which is a combination of longshore and cross-shore models, is used. The Nha Trang beach morphology is driven by a tropical wave climate dominated by seasonal variations and winter monsoon intra-seasonal pulses. The combined model accounts for seasonal shoreline evolution, which is primarily attributed to cross-shore dynamics but fails to represent accretion that occurs during the height of summer under low energy conditions. The reason is in the single equilibrium Dean number Ωeq of the ShoreFor model, one of the components of the combined model. This equilibrium Dean number cannot simultaneously account for the evolution of strong intra-seasonal events (i.e., winter monsoon pulses) and the annual recovery mechanisms associated with swash transport. By assigning a constant value to Ωeq, when the surf similarity parameter is higher than 3.3 (occurrence of small surging breakers in summer), we strongly improve the shoreline position prediction. This clearly points to the relevance of a multi-scale approach, although our modified Ωeq retains the advantage of simplicity.

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Modelling cross-shore shoreline change on multiple timescales and their interactions

Cited by 3 publications

References 48 publications

Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

An Integrated Machine Learning-Probabilistic Approach to Predict Beach Volume Change

Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

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