Wave storm dynamics and clustering, and their impacts on beach erosion
Salika Thilakarathne, Hesamodin Enayatighadikolaei, Md Shofiqul Islam, Takayuki Suzuki, Martin Mäll, Ralf Weisse, Tran Van Phong, Tran Van Phong
TL;DR
This study examines how wave storms and their clustering affect beach erosion using 36 years of data from Japan and evaluates the effectiveness of machine learning models in predicting erosion.
Contribution
The study provides new insights into the seasonal and clustered behavior of wave storms and their impact on beach erosion, alongside an evaluation of ML models for erosion prediction.
Findings
Wave storm clustering and seasonal variations significantly influence beach erosion patterns.
Support vector regression (SVR) and deep neural networks (DNN) showed limited predictive accuracy for shoreline change.
Observed data revealed distinct erosional behaviors linked to clustered storm events.
Abstract
We analyse a 36-year hydrodynamic and morphological dataset from the Hasaki coast, Japan, comprising 501 wave storm events (405 individual and 96 clustered events) to investigate the impact of storm dynamics and clustering on beach erosion. Focusing on the wave component of storms, events are identified using wave height thresholds. Daily and weekly beach profile measurements from the Hasaki Oceanographic Research Station are used to quantify erosion. The study examines the seasonal influences on Hasaki beach, the characteristics and temporal evolution of storms, and their associated erosional impacts. Moreover, we test two supervised machine learning (ML) algorithms, support vector regression (SVR), and deep neural network (DNN), in predicting shoreline change using 16 wave, storm, and morphological features. SVR showed reasonable accuracy on the training dataset but underperformed on…
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Taxonomy
TopicsCoastal and Marine Dynamics