Extreme Inundation Statistics on a Composite Beach

Abdalazeez, Ahmed; Didenkulova, Ira; Dutykh, Denys; Labart, Céline

doi:10.3390/w12061573

Water

2020

DOI: 10.3390/w12061573

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Extreme Inundation Statistics on a Composite Beach

Ahmed Abdalazeez

Ira Didenkulova

Denys Dutykh

et al.

Abstract: The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach of constant slope. To address different levels of nonlinearity, time series with five different significant wave heights are considered. The selected wave parameters allow for also seeing the effects of wave breaki… Show more

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Cited by 2 publications

References 31 publications

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A comparative analysis of machine learning algorithms for predicting wave runup

Durap

2023

Anthropocene Coasts

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The present study uses nine machine learning (ML) methods to predict wave runup in an innovative and comprehensive methodology. Unlike previous investigations, which often limited the factors considered when applying ML methodologies to predict wave runup, this approach takes a holistic perspective. The analysis takes into account a comprehensive range of crucial coastal parameters, including the 2% exceedance value for runup, setup, total swash excursion, incident swash, infragravity swash, significant wave height, peak wave period, foreshore beach slope, and median sediment size. Model performance, interpretability, and practicality were assessed. The findings from this study showes that linear models, while valuable in many applications, proved insufficient in grasping the complexity of this dataset. On the other hand, we found that non-linear models are essential for achieving accurate wave runup predictions, underscoring their significance in the context of the research. Within the framework of this examination, it was found that wave runup is affected by median sediment size, significant wave height, and foreshore beach slope. Coastal engineers and managers can utilize these findings to design more resilient coastal structures and evaluate the risks posed by coastal hazards. To improve forecast accuracy, the research stressed feature selection and model complexity management. This research proves machine learning algorithms can predict wave runup, aiding coastal engineering and management. These models help build coastal infrastructure and predict coastal hazards. Graphical Abstract

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A comparative analysis of machine learning algorithms for predicting wave runup

Durap

2023

Anthropocene Coasts

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Analysis of Monthly Recorded Climate Extreme Events and Their Implications on the Spanish Mediterranean Coast

Juan

Valdecantos

Campo

2022

Water

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Due to climate change, hydroclimatic extremes are becoming more frequent and intense and their characterization and analysis is essential for climate modelling. One of the regions that will be most affected by these extremes is the Mediterranean coast of Spain. Therefore, this paper analyses the significant wave height (Hs), peak wave period (Tp) and sea level (SL) extremes and their correlation along the Spanish Mediterranean coast. After conducting this analysis, it is finally concluded that and adjustment of the extreme long-term distribution of Tp is urgently needed to create accurate models and projections, which must be considered in combination with the intense extremes happening in the Levantine basin when modelling this area and designing new projects.

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Extreme Inundation Statistics on a Composite Beach

Cited by 2 publications

References 31 publications

A comparative analysis of machine learning algorithms for predicting wave runup

A comparative analysis of machine learning algorithms for predicting wave runup

Analysis of Monthly Recorded Climate Extreme Events and Their Implications on the Spanish Mediterranean Coast

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