%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Disdier, E.
%A Almar, Rafaël
%A Benshila, R.
%A Al Najar, M.
%A Chassagne, R.
%A Mukherjee, D.
%A Wilson, D. G.
%T Predicting beach profiles with machine learning from offshore wave reflection spectra
%D 2025
%L fdi:010091883
%G ENG
%J Environmental Modelling and Software
%@ 1364-8152
%K Machine learning ; ANN ; Bathymetry ; Sand bars ; Beach slope ; Wave reflection
%M ISI:001325014700001
%P 106221 [11 ]
%R 10.1016/j.envsoft.2024.106221
%U https://www.documentation.ird.fr/hor/fdi:010091883
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-11/010091883.pdf
%V 183
%W Horizon (IRD)
%X Tracking and forecasting changes in coastal morphology is vital for development, risk reduction, and overall coastal management. One challenge of current coastal research and engineering is to find a method able to accurately assess the bathymetry profile along the coast and key parameters such as slope and sandbars. Traditional bathymetry measurements are obtained through echo-sounding, which is time-consuming, hazardous and costly. Using a variety of simulated cases, we test the potential of machine learning and in particular Neural Networks to reconstruct the coastal bathymetry profile from offshore sensed waves, based on shore-based wave reflection. Features such as foreshore slope, curvature, sandbars amplitude and positions can be captured.
%$ 021 ; 064 ; 032 ; 020