# Wave storm dynamics and clustering, and their impacts on beach erosion

## 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.

## Key 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 testing, while DNN failed to produce reliable predictions on both. With SVR yielding an R2 of 0.18 and DNN 0.27 on the testing dataset, we conclude that, given the limited data and available features, such ML models may not generalise well. However, separate analyses using observed data reveal clear seasonal variations in wave storm dynamics and distinct behaviours of clustered events associated with beach erosion, highlighting important insights beyond the ML results.