Prediction of offshore bar-shape parameters resulted by cross-shore sediment transport using neural network

Kömürcü, Murat İhsan; Komur, M. Aydin; Akpınar, Adem; Özölçer, İsmail Hakkı; Yüksek, Ömer

doi:10.1016/j.apor.2013.01.003

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…In the last decade, artificial neural networks (ANNs) have been increasingly employed in coastal engineering and management applications due to their effectiveness in modeling complex non-linear systems with great speed. ANNs have been employed for predictions of beach seasonal changes (Hashemi et al, 2010), longshore sediment transport (Kabiri-Samani et al, 2011;Güner et al, 2013), sandbar characteristics (Kömürcü et al, 2013;López et al, 2017), storm surge (Kim et al, 2015) and coastal overtopping (van Gent et al, 2007;Verhaeghe et al, 2008;Chondros et al, 2021). Of particular relevance to this study, Santos et al (2019) tested the use of ANNs, among other statistical models, to predict changes in dune geometry during storms at Dauphin Island in the USA.…”

Section: Introductionmentioning

confidence: 99%

Estimating dune erosion at the regional scale using a meta-model based on neural networks

Athanasiou

Dongeren

Giardino³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Sandy beaches and dune systems have high recreational and ecological value, and they offer protection against flooding during storms. At the same time, these systems are very vulnerable to storm impacts. Process-based numerical models are presently used to assess the morphological changes of dune and beach systems during storms. However, such models come with high computational costs, hindering their use in real-life applications which demand many simulations and/or involve a large spatial–temporal domain. Here we design a novel meta-model to predict dune erosion volume (DEV) at the Dutch coast, based on artificial neural networks (ANNs), trained with cases from process-based modeling. First, we reduce an initial database of ∼1400 observed sandy profiles along the Dutch coastline to 100 representative typological coastal profiles (TCPs). Next, we synthesize a set of plausible extreme storm events, which reproduces the probability distributions and statistical dependencies of offshore wave and water level records. We choose 100 of these events to simulate the dune response of the 100 TCPs using the process-based model XBeach, resulting in 10 000 cases. Using these cases as training data, we design a two-phase meta-model, comprised of a classifying ANN (which predicts the occurrence (or not) of erosion) and a regression ANN (which gives a DEV prediction). Validation against a benchmark dataset created with XBeach and a sparse set of available dune erosion observations shows high prediction skill with a skill score of 0.82. The meta-model can predict post-storm DEV 103–104 times faster (depending on the duration of the storm) than running XBeach. Hence, this model may be integrated in early warning systems or allow coastal engineers and managers to upscale storm forcing to dune response investigations to large coastal areas with relative ease.

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Section: Introductionmentioning

confidence: 99%

Estimating dune erosion at the regional scale using a meta-model based on neural networks

Athanasiou

Dongeren

Giardino³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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“…The oceanographic prediction responding to the meteorological action of TC using Neural Networks involves the prediction of storm surge [26,[57][58][59], extreme waves [60][61][62][63][64][65], etc. The morphodynamical prediction responding to the oceanographic action of TC applies Neural Networks to sandbar movement [66][67][68], seasonal beach profile changes [69], and longshore sediment transport [70,71]. In addition to TC prediction, Neural Networks has been widely applied in the prediction of tidal level [72][73][74][75], wave height [61,76] and coastal floods [77].…”

Section: Introductionmentioning

confidence: 99%

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

et al. 2021

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As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.

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“…There are many approaches that have been used to model morphodynamic processes in coastal areas and hold promise to be utilized as surrogate models for dune erosion. For instance, Multivariate Adaptive Regression Splines (MARS) has been applied to simulate scour (Samadi et al, ) and landslides (Conoscenti et al, ); Artificial Neural Networks (ANNs) have been used to predict bar movement (Kömürcü et al, ; López et al, ; Pape et al, ), seasonal beach changes (Hashemi et al, ), and long‐shore sediment transport (Güner et al, ; Kabiri‐Samani et al, ); and Bayesian Networks (BNs) have been employed to predict coastal vulnerability to sea level rise (Gutierrez et al, ), barrier island morphodynamics (Gutierrez et al, ; Plant & Stockdon, ), and to model shoreline change (Beuzen et al, ; Plant et al, ). However, to the best of our knowledge, very few studies have attempted to develop surrogate models for coastal dune erosive processes.…”

Section: Introductionmentioning

confidence: 99%

Combining Numerical and Statistical Models to Predict Storm‐Induced Dune Erosion

et al. 2019

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Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early-warning systems. Based on a combination of probabilistic and process-based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL, in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k-fold approach for validation. All models perform well in predicting changes in dune elevation, barrier island area, and width but are less accurate in predicting alterations in the cross-shore locations of dune morphological features. Multivariate adaptive regression splines is identified as the best surrogate model based on its fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available. Key Points: • Surrogate models of XBeach are developed and tested, showing that multivariate adaptive regression splines exhibits the best performance • Carefully trained statistical and machine-learning models predict changes in barrier island geometric parameters at low computational cost • The highest performance is attained for the most extreme dune impact regimes, allowing fast and accurate dune height change predictions Correspondence to:Here, surrogate models are designed to predict coastal dune erosion by addressing issues related to computational performance and absence of sufficient forcing data. Our main goal is to understand complex morphological interactions through simulation of a wide range of realistic scenarios. To accomplish this, we develop and test surrogate models for XBeach that are trained with simulated, physically consistent oceanographic variables and associated dune response. This includes the following steps: (1) select a subset of synthetic multivariate sea storm events from Wahl et al. (2016) and consider time-dependent evolution of the oceanographic variables, (2) use XBeach to predict morphological response parameters for the selected sea storms, and (3) develop and test a range of different surrogate models to mimic XBeach at low computational cost. The different analysis steps are summarized in Figure 1. In...

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Prediction of offshore bar-shape parameters resulted by cross-shore sediment transport using neural network

Cited by 15 publications

References 31 publications

Estimating dune erosion at the regional scale using a meta-model based on neural networks

Estimating dune erosion at the regional scale using a meta-model based on neural networks

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

Combining Numerical and Statistical Models to Predict Storm‐Induced Dune Erosion

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