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A hybrid process-based modelling and machine learning approach to predict shoreline evolution


School of Engineering

About the Project

Shorelines constantly move in response to changes in wind, waves, tides, sediment supply, relative sea level and human activities over a variety of timescales. Thus, the implementation of effective coastal management strategies needs reliable information on coastal erosion and/or deposition processes. Up until now, the statistical analysis of shoreline data and application of process-based morphodynamic models are the main approaches that are used by engineers to predict shoreline changes at specific sites and develop management options. With the dramatic increase of available data on coastal systems in recent years a range of machine learning (ML) techniques involving various shoreline attributes have been applied to predict beach and shoreline changes under different forcing conditions. However, pure machine learning has limited explaining capability and may not be able to predict future trend outside the data domain used to train the ML models. The aim of this project is to develop a hybrid shoreline evolution model that combines the capability of machine learning techniques and solid physics built into process-based morphodynamic models by using the output of the ML model to inform the input to the process-base model through an efficient iterative procedure.

Applicant Eligibility

Candidates will have, or be due to obtain, a Master’s Degree or equivalent from a reputable University in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered. 

Application Process

Candidates wishing to apply should complete the University of Liverpool application form which can be accessed here: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ applying for a PhD in Engineering and uploading: Degree Certificates & Transcripts, an up to date CV, a covering letter/personal statement and two academic references. If a candidate wishes to apply for more than one project, should also upload a document listing the preferred projects in a ranked order. 

Enquiries

Candidates wishing to discuss the research project should contact the primary supervisor, those wishing to discuss the application process should discuss this with the School Postgraduate Office []. 


Funding Notes

This Scholarship is for UK [home] students only and has a financial package including: annual stipend at the UKRI rate [currently £15,285 per annum], student fees and a research support grant [for conferences & travel, consumables etc] for 3.5 academic years. The funding has been available from the following endowments of the School of Engineering: Kitchner Fellowship, Munitions Fellowship, Mrs W M Horsman Endowment and Andrew Hamilton Endowment.

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