Predicting shoreline change using deep learning methods

Coasts are changing and it’s important to be able to predict how the position of the shoreline moves over time.

We are supporting PhD researcher Tharindu Manamperi at Swansea University in his work on predicting shoreline change over multiple time scales using deep learning methods, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) models.

Tharindu’s pre-print paper on his latest work is open access, so anyone can read it for free: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5291473 

Citation

Manamperi, Tharindu and Rahat, Alma and Pender, Doug and Cristaudo, Demetra and Lamb, Rob and Karunarathna, Harshinie, Predicting Shoreline Changes Using Deep Learning Techniques with Bayesian Optimisation. Available at SSRN: https://ssrn.com/abstract=5291473

Funding and support

Tharindu’s research is supported by the UK & Engineering and Physical Sciences Research Council (EPSRC) – Doctoral Training Partnerships (DTP) (EP/W524694/1) and JBA Trust (project No. W22-1128),

Tharindu is supervised by Professor Harshinie Karunarathna and Dr Alma Rahat (Swansea University), and Dr Doug Pender and Dr Demetra Cristaudo (JBA Consulting)