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\begin{document}

\title{Predicting morphological changes along a macrotidal coastline Using a
Two-Stage Machine Learning Model}



\author[1]{Pavitra Kumar}%
\author[1]{Nicoletta Leonardi}%
\affil[1]{University of Liverpool}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Within the context of climate change, understanding and predicting
coastal change is of the foremost importance to protect coastal
communities and coastal assets. This study analyzes field data from 125
locations along the Morecambe coastline, consisting of beach transects
collected twice a year for more than a decade (2007 to 2022). To model
the sediment volume changes observed along the Morecambe coastline, this
study proposes a two-stage machine learning model that incorporates
beach behavior classification and deep learning techniques to predict
changes in sediment volumes along coastal environments. The first stage
of the model, developed using a random forest classifier, classifies
beach behavior into four categories: eroding, accreting, stable, or
undergoing short-term fluctuations. The second stage of the model
developed using LSTM and sequence-to-sequence models, uses the output of
the first stage to predict the available sediment volume after
erosion/accretion. LSTM model achieved a testing regression of 0.9961
for one-step-ahead (6 months) predictions of sediment volume time
series, while sequence-to-sequence model achieved the testing regression
of 0.9950 for three-time-ahead (1.5 years) predictions and 0.9916 for
ten-time-step-ahead (5 years) prediction.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Manuscript.docx`  available at \url{https://authorea.com/users/655427/articles/788445-predicting-morphological-changes-along-a-macrotidal-coastline-using-a-two-stage-machine-learning-model}\selectlanguage{english}
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